diff --git a/.github/workflows/combined.yaml b/.github/workflows/combined.yaml
index 50f0aa3749..29cd3702a6 100644
--- a/.github/workflows/combined.yaml
+++ b/.github/workflows/combined.yaml
@@ -19,6 +19,8 @@ jobs:
     container: eco4cast/rocker-neon4cast:latest
     steps:
       - uses: actions/checkout@v3
+        with:
+          ref: prod
 
       - name: Process submissions
         shell: Rscript {0}
@@ -36,6 +38,8 @@ jobs:
     container: eco4cast/rocker-neon4cast:latest
     steps:
       - uses: actions/checkout@v3
+        with:
+          ref: prod
 
       - name: Generate scores
         shell: Rscript {0}
@@ -61,8 +65,9 @@ jobs:
 
       - uses: actions/checkout@v3
         with:
-           fetch-depth: 0
-           set-safe-directory: '*'
+          ref: prod
+          fetch-depth: 0
+          set-safe-directory: '*'
 
       - name: install validator
         run: |
@@ -91,8 +96,9 @@ jobs:
 
       - uses: actions/checkout@v3
         with:
-           fetch-depth: 0
-           set-safe-directory: '*'
+          ref: prod
+          fetch-depth: 0
+          set-safe-directory: '*'
 
       - uses: quarto-dev/quarto-actions/setup@v2
         with:
diff --git a/catalog/R/stac_functions.R b/catalog/R/stac_functions.R
index 9b303bb35c..e8d0fb15f9 100644
--- a/catalog/R/stac_functions.R
+++ b/catalog/R/stac_functions.R
@@ -16,9 +16,12 @@ generate_model_assets <- function(m_vars, m_duration, aws_path){
     "1"= list(
       'type'= 'application/json',
       'title' = 'Model Metadata',
-      'href' = paste0("https://", config$endpoint,"/", config$model_metadata_bucket,"/",m,".json"),
+      'href' = paste0("https://", config$endpoint,"/",
+                      config$model_metadata_bucket,"/",
+                      "project_id=",config$project_id, "/",
+                      m,".json"),
       'description' = paste0("Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.
-      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(",paste0('"','https://', config$endpoint,'/', config$model_metadata_bucket,'/',m,'.json"'),")\n\n")
+      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(",paste0('"','https://', config$endpoint,'/', config$model_metadata_bucket,'/', 'project_id=', config$project_id, '/', m,'.json"'),")\n\n")
     )
   )
 
@@ -516,8 +519,8 @@ build_group_variables <- function(table_schema,
                   ),
                   list(
                     "rel" = "describedby",
-                    "href" = "https://ltreb-reservoirs.github.io/vera4cast/",
-                    "title" = "VERA Forecast Challenge Dashboard",
+                    "href" = "https://ltreb-reservoirs.github.io/vera4cast/", # TODO: update this to something?
+                    "title" = "EFI-USGS Forecast Challenge Dashboard",
                     "type" = "text/html"
                   )
                 )),
diff --git a/catalog/forecasts/Aquatics/Daily_Dissolved_oxygen/collection.json b/catalog/forecasts/Aquatics/Daily_Dissolved_oxygen/collection.json
deleted file mode 100644
index b60918d809..0000000000
--- a/catalog/forecasts/Aquatics/Daily_Dissolved_oxygen/collection.json
+++ /dev/null
@@ -1,252 +0,0 @@
-{
-  "id": "Daily_Dissolved_oxygen",
-  "description": "This page includes all models for the Daily_Dissolved_oxygen variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/GLEON_lm_lag_1day.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/air2waterSat_2.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_f1.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_prophet.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/climatology.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/persistenceRW.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/null.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_bag_mlp.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Daily_Dissolved_oxygen",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-149.6106, 18.1135, -66.7987, 68.6698]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-01T00:00:00Z",
-          "2024-11-10T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-    },
-    {
-      "name": "parameter",
-      "type": "string",
-      "description": "ensemble member or distribution parameter"
-    },
-    {
-      "name": "prediction",
-      "type": "double",
-      "description": "predicted value for variable"
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique identifier for the forecast project"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "reference_date",
-      "type": "string",
-      "description": "date that the forecast was initiated"
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=oxygen?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=oxygen?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/forecasts/Aquatics/Daily_Water_temperature/collection.json b/catalog/forecasts/Aquatics/Daily_Water_temperature/collection.json
deleted file mode 100644
index 86062021d5..0000000000
--- a/catalog/forecasts/Aquatics/Daily_Water_temperature/collection.json
+++ /dev/null
@@ -1,322 +0,0 @@
-{
-  "id": "Daily_Water_temperature",
-  "description": "This page includes all models for the Daily_Water_temperature variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_bag_mlp.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/GLEON_JRabaey_temp_physics.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/GLEON_lm_lag_1day.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/GLEON_physics.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/air2waterSat_2.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/baseline_ensemble.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_f1.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_prophet.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/climatology.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/fARIMA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/fARIMA_clim_ensemble.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/fTSLM_lag.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flareGLM.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flareGLM_noDA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flareGOTM.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flareGOTM_noDA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flareSimstrat.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flareSimstrat_noDA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flare_ler.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flare_ler_baselines.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/persistenceRW.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/null.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Daily_Water_temperature",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-149.6106, 18.1135, -66.7987, 68.6698]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-01T00:00:00Z",
-          "2024-11-10T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-    },
-    {
-      "name": "parameter",
-      "type": "string",
-      "description": "ensemble member or distribution parameter"
-    },
-    {
-      "name": "prediction",
-      "type": "double",
-      "description": "predicted value for variable"
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique identifier for the forecast project"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "reference_date",
-      "type": "string",
-      "description": "date that the forecast was initiated"
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=temperature?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=temperature?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/summaries/Terrestrial/collection.json b/catalog/forecasts/aquatics/Daily_Chlorophyll_a/collection.json
similarity index 55%
rename from catalog/summaries/Terrestrial/collection.json
rename to catalog/forecasts/aquatics/Daily_Chlorophyll_a/collection.json
index 5966bac21c..c180808ec7 100644
--- a/catalog/summaries/Terrestrial/collection.json
+++ b/catalog/forecasts/aquatics/Daily_Chlorophyll_a/collection.json
@@ -1,6 +1,6 @@
 {
-  "id": "Terrestrial",
-  "description": "This page includes variables for the Terrestrial group.",
+  "id": "Daily_Chlorophyll_a",
+  "description": "This page includes all models for the Daily_Chlorophyll_a variable.",
   "stac_version": "1.0.0",
   "license": "CC0-1.0",
   "stac_extensions": [
@@ -11,24 +11,14 @@
   "type": "Collection",
   "links": [
     {
-      "rel": "child",
+      "rel": "item",
       "type": "application/json",
-      "href": "Daily_Net_ecosystem_exchange/collection.json"
+      "href": "../../models/model_items/climatology.json"
     },
     {
-      "rel": "child",
+      "rel": "item",
       "type": "application/json",
-      "href": "Daily_latent_heat_flux/collection.json"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "30min_Net_ecosystem_exchange/collection.json"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "30min_latent_heat_flux/collection.json"
+      "href": "../../models/model_items/persistenceRW.json"
     },
     {
       "rel": "parent",
@@ -51,29 +41,29 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
-  "title": "Terrestrial",
+  "title": "Daily_Chlorophyll_a",
   "extent": {
     "spatial": {
       "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
+        ["Inf", "Inf", "-Inf", "-Inf"]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2023-01-01T00:00:00Z",
-          "2024-12-09T00:00:00Z"
+          "2024-02-07T00:00:00Z",
+          "2024-03-14T00:00:00Z"
         ]
       ]
     }
@@ -84,60 +74,35 @@
       "type": "timestamp[us, tz=UTC]",
       "description": "datetime that the forecast was initiated (horizon = 0)"
     },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-    },
     {
       "name": "datetime",
       "type": "timestamp[us, tz=UTC]",
       "description": "datetime of the forecasted value (ISO 8601)"
     },
     {
-      "name": "family",
+      "name": "site_id",
       "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
+      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
     },
     {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
+      "name": "family",
+      "type": "string",
+      "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
     },
     {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
+      "name": "parameter",
+      "type": "string",
+      "description": "ensemble member or distribution parameter"
     },
     {
-      "name": "quantile90",
+      "name": "prediction",
       "type": "double",
-      "description": "upper 90 percentile value of forecast"
+      "description": "predicted value for variable"
     },
     {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
+      "name": "pub_datetime",
+      "type": "timestamp[us, tz=UTC]",
+      "description": "datetime that forecast was submitted"
     },
     {
       "name": "project_id",
@@ -167,21 +132,21 @@
   ],
   "assets": {
     "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org\"",
+      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=usgsrc4cast/duration=P1D/variable=chla?endpoint_override=sdsc.osn.xsede.org",
       "type": "application/x-parquet",
       "title": "Database Access",
       "roles": [
         "data"
       ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for the NEON Ecological Forecasting Aquatics theme.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |>\n dplyr::filter(variable %in% c(\"nee\", \"le\")) |>\n dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=usgsrc4cast/duration=P1D/variable=chla?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     },
     "thumbnail": {
-      "href": "https://projects.ecoforecast.org/neon4cast-catalog/img/BONA_Twr.jpg",
+      "href": "pending",
       "type": "image/JPEG",
       "roles": [
         "thumbnail"
       ],
-      "title": "NEON Field Tower"
+      "title": "pending"
     }
   }
 }
diff --git a/catalog/summaries/Beetles/collection.json b/catalog/forecasts/aquatics/collection.json
similarity index 60%
rename from catalog/summaries/Beetles/collection.json
rename to catalog/forecasts/aquatics/collection.json
index 77945c89aa..57b5da1262 100644
--- a/catalog/summaries/Beetles/collection.json
+++ b/catalog/forecasts/aquatics/collection.json
@@ -1,6 +1,6 @@
 {
-  "id": "Beetles",
-  "description": "This page includes variables for the Beetles group.",
+  "id": "aquatics",
+  "description": "This page includes variables for the aquatics group.",
   "stac_version": "1.0.0",
   "license": "CC0-1.0",
   "stac_extensions": [
@@ -13,12 +13,7 @@
     {
       "rel": "child",
       "type": "application/json",
-      "href": "Weekly_beetle_community_abundance/collection.json"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "Weekly_beetle_community_richness/collection.json"
+      "href": "Daily_Chlorophyll_a/collection.json"
     },
     {
       "rel": "parent",
@@ -41,29 +36,29 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
-  "title": "Beetles",
+  "title": "aquatics",
   "extent": {
     "spatial": {
       "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
+        ["Inf", "Inf", "-Inf", "-Inf"]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2023-01-01T00:00:00Z",
-          "2024-12-09T00:00:00Z"
+          "2024-02-07T00:00:00Z",
+          "2024-03-14T00:00:00Z"
         ]
       ]
     }
@@ -74,60 +69,35 @@
       "type": "timestamp[us, tz=UTC]",
       "description": "datetime that the forecast was initiated (horizon = 0)"
     },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-    },
     {
       "name": "datetime",
       "type": "timestamp[us, tz=UTC]",
       "description": "datetime of the forecasted value (ISO 8601)"
     },
     {
-      "name": "family",
+      "name": "site_id",
       "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
+      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
     },
     {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
+      "name": "family",
+      "type": "string",
+      "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
     },
     {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
+      "name": "parameter",
+      "type": "string",
+      "description": "ensemble member or distribution parameter"
     },
     {
-      "name": "quantile90",
+      "name": "prediction",
       "type": "double",
-      "description": "upper 90 percentile value of forecast"
+      "description": "predicted value for variable"
     },
     {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
+      "name": "pub_datetime",
+      "type": "timestamp[us, tz=UTC]",
+      "description": "datetime that forecast was submitted"
     },
     {
       "name": "project_id",
@@ -157,21 +127,21 @@
   ],
   "assets": {
     "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org\"",
+      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/?endpoint_override=sdsc.osn.xsede.org",
       "type": "application/x-parquet",
       "title": "Database Access",
       "roles": [
         "data"
       ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for the NEON Ecological Forecasting Aquatics theme.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |>\n dplyr::filter(variable %in% c(\"abundance\", \"richness\")) |>\n dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `arrow` for remote access to the database. This R code will return results for the NEON Ecological Forecasting Aquatics theme.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |>\n dplyr::filter(variable %in% c(\"chla\")) |>\n dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     },
     "thumbnail": {
-      "href": "https://www.neonscience.org/sites/default/files/styles/max_width_1170px/public/image-content-images/Beetles_pinned.jpg",
+      "href": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/thumbnails/image/Back-b.jpg",
       "type": "image/JPEG",
       "roles": [
         "thumbnail"
       ],
-      "title": "Beetle Image"
+      "title": "USGS Streamgage"
     }
   }
 }
diff --git a/catalog/forecasts/collection.json b/catalog/forecasts/collection.json
index 15a6dccacf..4dc66e61a6 100644
--- a/catalog/forecasts/collection.json
+++ b/catalog/forecasts/collection.json
@@ -13,32 +13,8 @@
     {
       "rel": "child",
       "type": "application/json",
-      "href": "Aquatics/collection.json",
-      "title": "Aquatics"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "Terrestrial/collection.json",
-      "title": "Terrestrial"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "Phenology/collection.json",
-      "title": "Phenology"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "Beetles/collection.json",
-      "title": "Beetles"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "Ticks/collection.json",
-      "title": "Ticks"
+      "href": "aquatics/collection.json",
+      "title": "aquatics"
     },
     {
       "rel": "child",
@@ -67,14 +43,14 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
@@ -83,18 +59,18 @@
     "spatial": {
       "bbox": [
         [
-          -156.6194,
-          17.9696,
-          -66.7987,
-          71.2824
+          -122.6692,
+          39.6327,
+          -74.7781,
+          45.5175
         ]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2023-01-01T00:00:00Z",
-          "2024-12-09T00:00:00Z"
+          "2024-02-07T00:00:00Z",
+          "2024-03-14T00:00:00Z"
         ]
       ]
     }
@@ -163,21 +139,21 @@
   ],
   "assets": {
     "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/?endpoint_override=sdsc.osn.xsede.org\"",
+      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/?endpoint_override=sdsc.osn.xsede.org",
       "type": "application/x-parquet",
       "title": "Database Access",
       "roles": [
         "data"
       ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for the VERA Forecasting Challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `arrow` for remote access to the database. This R code will return results for the Forecasting Challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     },
     "thumbnail": {
-      "href": "https://raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/thumbnail_plots/51629805865_0ef01ffbbc_c.jpg",
+      "href": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/thumbnails/image/Back-b.jpg",
       "type": "image/JPEG",
       "roles": [
         "thumbnail"
       ],
-      "title": "NEON Buoy Image"
+      "title": "USGS Streamgage"
     }
   }
 }
diff --git a/catalog/forecasts/forecast_models.R b/catalog/forecasts/forecast_models.R
index 56a4455c8c..7c964342df 100644
--- a/catalog/forecasts/forecast_models.R
+++ b/catalog/forecasts/forecast_models.R
@@ -35,7 +35,8 @@ forecast_description_create <- data.frame(datetime = 'datetime of the forecasted
 # model_id <- 'climatology'
 
 print('FIND FORECAST TABLE SCHEMA')
-forecast_theme_df <- arrow::open_dataset(arrow::s3_bucket(config$forecasts_bucket, endpoint_override = config$endpoint, anonymous = TRUE)) #|>
+forecast_theme_df <- arrow::open_dataset(arrow::s3_bucket(config$forecasts_bucket,
+                                                          endpoint_override = config$endpoint, anonymous = TRUE)) #|>
   #filter(model_id == model_id, site_id = site_id, reference_datetime = reference_datetime)
 # NOTE IF NOT USING FILTER -- THE stac4cast::build_table_columns() NEEDS TO BE UPDATED
     #(USE strsplit(forecast_theme_df$ToString(), "\n") INSTEAD OF strsplit(forecast_theme_df[[1]]$ToString(), "\n"))
diff --git a/catalog/forecasts/models/collection.json b/catalog/forecasts/models/collection.json
index d58c5f483b..61c0eac9f7 100644
--- a/catalog/forecasts/models/collection.json
+++ b/catalog/forecasts/models/collection.json
@@ -10,16 +10,6 @@
   ],
   "type": "Collection",
   "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/USGSHABs1.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/cb_prophet.json"
-    },
     {
       "rel": "item",
       "type": "application/json",
@@ -30,236 +20,6 @@
       "type": "application/json",
       "href": "model_items/persistenceRW.json"
     },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procBlanchardMonod.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procBlanchardSteele.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procCTMIMonod.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procCTMISteele.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procEppleyNorbergMonod.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procEppleyNorbergSteele.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procHinshelwoodSteele.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_bag_mlp.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_lasso_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/prophet_clim_ensemble.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/GLEON_lm_lag_1day.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/air2waterSat_2.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/cb_f1.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/mean.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_randfor_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/GLEON_JRabaey_temp_physics.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/GLEON_physics.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/baseline_ensemble.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/fARIMA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/fARIMA_clim_ensemble.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/fTSLM_lag.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flareGLM.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flareGLM_noDA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flareGOTM.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flareGOTM_noDA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flareSimstrat.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flareSimstrat_noDA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flare_ler.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flare_ler_baselines.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/null.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procHinshelwoodMonod.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/USUNEEDAILY.json"
-    },
     {
       "rel": "parent",
       "type": "application/json",
@@ -281,14 +41,14 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
@@ -296,14 +56,14 @@
   "extent": {
     "spatial": {
       "bbox": [
-        [-156.6194, 17.9696, -66.7987, 71.2824]
+        [-122.6692, 39.6328, -74.7781, 45.5175]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2023-01-01T00:00:00Z",
-          "2024-12-09T00:00:00Z"
+          "2024-02-07T00:00:00Z",
+          "2024-03-14T00:00:00Z"
         ]
       ]
     }
@@ -372,7 +132,7 @@
   ],
   "assets": {
     "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/?endpoint_override=sdsc.osn.xsede.org\"",
+      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/?endpoint_override=sdsc.osn.xsede.org",
       "type": "application/x-parquet",
       "title": "Database Access",
       "roles": [
diff --git a/catalog/forecasts/models/model_items/.empty b/catalog/forecasts/models/model_items/.empty
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/catalog/forecasts/models/model_items/GLEON_JRabaey_temp_physics.json b/catalog/forecasts/models/model_items/GLEON_JRabaey_temp_physics.json
deleted file mode 100644
index 415fc5bc3c..0000000000
--- a/catalog/forecasts/models/model_items/GLEON_JRabaey_temp_physics.json
+++ /dev/null
@@ -1,199 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "GLEON_JRabaey_temp_physics",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-18",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "GLEON_JRabaey_temp_physics"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "GLEON_JRabaey_temp_physics"
-    },
-    {
-      "rel": "self",
-      "href": "GLEON_JRabaey_temp_physics.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_JRabaey_temp_physics.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_JRabaey_temp_physics.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=GLEON_JRabaey_temp_physics?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=GLEON_JRabaey_temp_physics?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/GLEON_lm_lag_1day.json b/catalog/forecasts/models/model_items/GLEON_lm_lag_1day.json
deleted file mode 100644
index b809aefbc0..0000000000
--- a/catalog/forecasts/models/model_items/GLEON_lm_lag_1day.json
+++ /dev/null
@@ -1,179 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "GLEON_lm_lag_1day",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Dissolved_oxygen, Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "GLEON_lm_lag_1day"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "GLEON_lm_lag_1day"
-    },
-    {
-      "rel": "self",
-      "href": "GLEON_lm_lag_1day.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_lm_lag_1day.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_lm_lag_1day.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/GLEON_physics.json b/catalog/forecasts/models/model_items/GLEON_physics.json
deleted file mode 100644
index b66ac9b45e..0000000000
--- a/catalog/forecasts/models/model_items/GLEON_physics.json
+++ /dev/null
@@ -1,171 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "GLEON_physics",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-22",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "GLEON_physics"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "GLEON_physics"
-    },
-    {
-      "rel": "self",
-      "href": "GLEON_physics.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_physics.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_physics.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=GLEON_physics?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=GLEON_physics?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/USGSHABs1.json b/catalog/forecasts/models/model_items/USGSHABs1.json
deleted file mode 100644
index 3d22895f41..0000000000
--- a/catalog/forecasts/models/model_items/USGSHABs1.json
+++ /dev/null
@@ -1,168 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "USGSHABs1",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-87.7982, 32.5415],
-      [-88.1589, 31.8534],
-      [-84.4374, 31.1854]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BLWA, TOMB, FLNT\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-12",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "USGSHABs1"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "USGSHABs1"
-    },
-    {
-      "rel": "self",
-      "href": "USGSHABs1.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/USGSHABs1.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/USGSHABs1.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=USGSHABs1?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=USGSHABs1?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/USUNEEDAILY.json b/catalog/forecasts/models/model_items/USUNEEDAILY.json
deleted file mode 100644
index e6af8e844b..0000000000
--- a/catalog/forecasts/models/model_items/USUNEEDAILY.json
+++ /dev/null
@@ -1,166 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "USUNEEDAILY",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-155.3173, 19.5531]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: \"Home brew ARIMA.\" We didn't use a formal time series framework because of all the missing values in both our response variable and the weather covariates. So we used a GAM to fit a seasonal component based on day of year, and we included NEE the previous day as as an AR 1 term. We did some model selection, using cross validation, to identify temperature and relative humidity as weather covariates.\n\nSites: PUUM\n\nVariables: Daily Net_ecosystem_exchange",
-    "start_datetime": "2023-12-12",
-    "end_datetime": "2024-01-16",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Net_ecosystem_exchange"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "USUNEEDAILY"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "USUNEEDAILY"
-    },
-    {
-      "rel": "self",
-      "href": "USUNEEDAILY.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/USUNEEDAILY.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/USUNEEDAILY.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=USUNEEDAILY?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=USUNEEDAILY?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/air2waterSat_2.json b/catalog/forecasts/models/model_items/air2waterSat_2.json
deleted file mode 100644
index 3447344a32..0000000000
--- a/catalog/forecasts/models/model_items/air2waterSat_2.json
+++ /dev/null
@@ -1,206 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "air2waterSat_2",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, TOOK, WALK, WLOU\n\nVariables: Daily Dissolved_oxygen, Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-20",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "air2waterSat_2"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "air2waterSat_2"
-    },
-    {
-      "rel": "self",
-      "href": "air2waterSat_2.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/air2waterSat_2.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/air2waterSat_2.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/baseline_ensemble.json b/catalog/forecasts/models/model_items/baseline_ensemble.json
deleted file mode 100644
index ba122f0cfd..0000000000
--- a/catalog/forecasts/models/model_items/baseline_ensemble.json
+++ /dev/null
@@ -1,241 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "baseline_ensemble",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-87.7982, 32.5415],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-72.1727, 42.5369],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-99.1066, 47.1617],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-122.3303, 45.7624],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BLWA, COMO, CUPE, FLNT, GUIL, HOPB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, MCDI, MCRA, POSE, PRIN, REDB, ARIK, BARC, BIGC, BLDE, BLUE, KING, LECO, LEWI, MART, MAYF, LENO, MLBS, MOAB, NIWO, NOGP, OAES, HARV, JERC, JORN, KONA, KONZ, LAJA, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, SCBI, SERC, SJER, SOAP, SRER, ONAQ, ORNL, OSBS, PUUM, RMNP, DCFS, DELA, DSNY, GRSM, GUAN, ABBY, BART, BLAN, CLBJ, CPER\n\nVariables: Daily Water_temperature, Daily Red_chromatic_coordinate",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature",
-      "Daily Red_chromatic_coordinate"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "baseline_ensemble"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "baseline_ensemble"
-    },
-    {
-      "rel": "self",
-      "href": "baseline_ensemble.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/baseline_ensemble.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/baseline_ensemble.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/cb_f1.json b/catalog/forecasts/models/model_items/cb_f1.json
deleted file mode 100644
index 0c78857632..0000000000
--- a/catalog/forecasts/models/model_items/cb_f1.json
+++ /dev/null
@@ -1,206 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "cb_f1",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-10-11",
-    "end_datetime": "2024-11-10",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "cb_f1"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "cb_f1"
-    },
-    {
-      "rel": "self",
-      "href": "cb_f1.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/cb_f1.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/cb_f1.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=cb_f1?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=cb_f1?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=cb_f1?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=cb_f1?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/cb_prophet.json b/catalog/forecasts/models/model_items/cb_prophet.json
deleted file mode 100644
index a738acd166..0000000000
--- a/catalog/forecasts/models/model_items/cb_prophet.json
+++ /dev/null
@@ -1,286 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "cb_prophet",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-89.5373, 46.2339],
-      [-103.0293, 40.4619],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-119.2622, 37.0334],
-      [-89.5864, 45.5089],
-      [-87.3933, 32.9505],
-      [-149.3705, 68.6611],
-      [-99.2413, 47.1282],
-      [-110.8355, 31.9107],
-      [-121.9519, 45.8205],
-      [-119.006, 37.0058],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, UNDE, STER, TREE, UKFS, SOAP, STEI, TALL, TOOL, WOOD, SRER, WREF, TEAK, YELL, ARIK, BIGC, BLDE, BLUE, CARI, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU, COMO, CUPE\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily latent_heat_flux, Daily Red_chromatic_coordinate, Daily Dissolved_oxygen, Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily latent_heat_flux",
-      "Daily Red_chromatic_coordinate",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "cb_prophet"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "cb_prophet"
-    },
-    {
-      "rel": "self",
-      "href": "cb_prophet.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/cb_prophet.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/cb_prophet.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/climatology.json b/catalog/forecasts/models/model_items/climatology.json
index df276a7ae6..1dfb280dc3 100644
--- a/catalog/forecasts/models/model_items/climatology.json
+++ b/catalog/forecasts/models/model_items/climatology.json
@@ -7,96 +7,30 @@
   "id": "climatology",
   "bbox": [
     [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
+      -122.6692,
+      45.5175,
+      -74.7781,
+      45.5175
     ]
   ],
   "geometry": {
     "type": "MultiPoint",
     "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-84.4374, 31.1854],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-122.3303, 45.7624],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-156.6194, 71.2824],
-      [-147.5026, 65.154],
-      [-145.7514, 63.8811],
-      [-149.2133, 63.8758],
-      [-149.3705, 68.6611],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-147.504, 65.1532]
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      []
     ]
   },
   "properties": {
-    "description": ["\nmodel info: Historical DOY mean and sd.  Assumes normal distribution\n\n\nSites: BARC, BLWA, FLNT, SUGG, TOMB, ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BARR, BONA, DEJU, HEAL, TOOL, ARIK, BIGC, BLDE, BLUE, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU, CARI\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, 30min Net_ecosystem_exchange, Daily latent_heat_flux, 30min latent_heat_flux, Daily Red_chromatic_coordinate, Daily Dissolved_oxygen, Daily Water_temperature", "\nmodel info: NA\n\nSites: BARC, BLWA, FLNT, SUGG, TOMB, ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BARR, BONA, DEJU, HEAL, TOOL, ARIK, BIGC, BLDE, BLUE, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU, CARI\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, 30min Net_ecosystem_exchange, Daily latent_heat_flux, 30min latent_heat_flux, Daily Red_chromatic_coordinate, Daily Dissolved_oxygen, Daily Water_temperature"],
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-22",
+    "description": "\nmodel info: Forecasts stream chlorophyll-a based on the historic average and standard deviation for that given site and day-of-year.\n\nSites: USGS-01427510, USGS-01463500, USGS-05543010, USGS-05553700, USGS-05558300, USGS-05586300, USGS-14181500, USGS-14211010, USGS-14211720\n\nVariables: Daily Chlorophyll_a",
+    "start_datetime": "2024-02-07",
+    "end_datetime": "2024-03-14",
     "providers": [
       {
         "url": "pending",
@@ -118,16 +52,8 @@
     "license": "CC0-1.0",
     "keywords": [
       "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "30min Net_ecosystem_exchange",
-      "Daily latent_heat_flux",
-      "30min latent_heat_flux",
-      "Daily Red_chromatic_coordinate",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature"
+      "usgsrc4cast",
+      "Daily Chlorophyll_a"
     ],
     "table:columns": [
       {
@@ -241,56 +167,8 @@
     "3": {
       "type": "application/x-parquet",
       "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for 30min Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=PT30M/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=PT30M/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for 30min latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=PT30M/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=PT30M/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "href": "s3://anonymous@bio230014-bucket01/challenges/forecastsproject_id=/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
+      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecastsproject_id=/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     }
   }
 }
diff --git a/catalog/forecasts/models/model_items/fARIMA.json b/catalog/forecasts/models/model_items/fARIMA.json
deleted file mode 100644
index 2b10849ac2..0000000000
--- a/catalog/forecasts/models/model_items/fARIMA.json
+++ /dev/null
@@ -1,199 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "fARIMA",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-10",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fARIMA"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fARIMA"
-    },
-    {
-      "rel": "self",
-      "href": "fARIMA.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fARIMA.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fARIMA.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=fARIMA?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=fARIMA?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/fARIMA_clim_ensemble.json b/catalog/forecasts/models/model_items/fARIMA_clim_ensemble.json
deleted file mode 100644
index 6d95ee3f13..0000000000
--- a/catalog/forecasts/models/model_items/fARIMA_clim_ensemble.json
+++ /dev/null
@@ -1,193 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "fARIMA_clim_ensemble",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-96.443, 38.9459],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-122.1655, 44.2596],
-      [-83.5038, 35.6904],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-110.5871, 44.9501],
-      [-105.5442, 40.035],
-      [-119.2575, 37.0597],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-96.6038, 39.1051],
-      [-88.1589, 31.8534],
-      [-119.0274, 36.9559]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: MCDI, POSE, PRIN, REDB, SUGG, SYCA, WALK, WLOU, LEWI, MART, MAYF, MCRA, LECO, ARIK, BARC, BLUE, BLWA, CUPE, GUIL, HOPB, BLDE, COMO, BIGC, CRAM, FLNT, KING, TOMB, TECR\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-10",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fARIMA_clim_ensemble"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fARIMA_clim_ensemble"
-    },
-    {
-      "rel": "self",
-      "href": "fARIMA_clim_ensemble.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fARIMA_clim_ensemble.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fARIMA_clim_ensemble.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=fARIMA_clim_ensemble?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=fARIMA_clim_ensemble?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/fTSLM_lag.json b/catalog/forecasts/models/model_items/fTSLM_lag.json
deleted file mode 100644
index a99ef8d2d7..0000000000
--- a/catalog/forecasts/models/model_items/fTSLM_lag.json
+++ /dev/null
@@ -1,199 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "fTSLM_lag",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-10",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fTSLM_lag"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fTSLM_lag"
-    },
-    {
-      "rel": "self",
-      "href": "fTSLM_lag.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fTSLM_lag.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fTSLM_lag.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=fTSLM_lag?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=fTSLM_lag?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/flareGLM.json b/catalog/forecasts/models/model_items/flareGLM.json
deleted file mode 100644
index aede072b1e..0000000000
--- a/catalog/forecasts/models/model_items/flareGLM.json
+++ /dev/null
@@ -1,172 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flareGLM",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2024-01-16",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGLM"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGLM"
-    },
-    {
-      "rel": "self",
-      "href": "flareGLM.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGLM.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGLM.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=flareGLM?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=flareGLM?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/flareGLM_noDA.json b/catalog/forecasts/models/model_items/flareGLM_noDA.json
deleted file mode 100644
index bbf90f218a..0000000000
--- a/catalog/forecasts/models/model_items/flareGLM_noDA.json
+++ /dev/null
@@ -1,172 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flareGLM_noDA",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307],
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: PRPO, SUGG, TOOK, BARC, CRAM, LIRO, PRLA\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2024-01-16",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGLM_noDA"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGLM_noDA"
-    },
-    {
-      "rel": "self",
-      "href": "flareGLM_noDA.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGLM_noDA.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGLM_noDA.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=flareGLM_noDA?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=flareGLM_noDA?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/flareGOTM.json b/catalog/forecasts/models/model_items/flareGOTM.json
deleted file mode 100644
index b4a925cbae..0000000000
--- a/catalog/forecasts/models/model_items/flareGOTM.json
+++ /dev/null
@@ -1,171 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flareGOTM",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: PRPO, SUGG, BARC, CRAM, LIRO, PRLA\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2024-01-16",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGOTM"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGOTM"
-    },
-    {
-      "rel": "self",
-      "href": "flareGOTM.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGOTM.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGOTM.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=flareGOTM?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=flareGOTM?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/flareGOTM_noDA.json b/catalog/forecasts/models/model_items/flareGOTM_noDA.json
deleted file mode 100644
index 9180ee15f5..0000000000
--- a/catalog/forecasts/models/model_items/flareGOTM_noDA.json
+++ /dev/null
@@ -1,171 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flareGOTM_noDA",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: PRPO, SUGG, BARC, CRAM, LIRO, PRLA\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2024-01-16",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGOTM_noDA"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGOTM_noDA"
-    },
-    {
-      "rel": "self",
-      "href": "flareGOTM_noDA.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGOTM_noDA.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGOTM_noDA.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=flareGOTM_noDA?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=flareGOTM_noDA?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/flareSimstrat.json b/catalog/forecasts/models/model_items/flareSimstrat.json
deleted file mode 100644
index b86384bf91..0000000000
--- a/catalog/forecasts/models/model_items/flareSimstrat.json
+++ /dev/null
@@ -1,171 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flareSimstrat",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-82.0084, 29.676]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: PRPO, SUGG, CRAM, LIRO, PRLA, BARC\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2024-01-16",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareSimstrat"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareSimstrat"
-    },
-    {
-      "rel": "self",
-      "href": "flareSimstrat.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareSimstrat.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareSimstrat.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=flareSimstrat?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=flareSimstrat?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/flareSimstrat_noDA.json b/catalog/forecasts/models/model_items/flareSimstrat_noDA.json
deleted file mode 100644
index 38268d2b22..0000000000
--- a/catalog/forecasts/models/model_items/flareSimstrat_noDA.json
+++ /dev/null
@@ -1,170 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flareSimstrat_noDA",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, PRLA, PRPO, SUGG\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2024-01-16",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareSimstrat_noDA"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareSimstrat_noDA"
-    },
-    {
-      "rel": "self",
-      "href": "flareSimstrat_noDA.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareSimstrat_noDA.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareSimstrat_noDA.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=flareSimstrat_noDA?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=flareSimstrat_noDA?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/flare_ler.json b/catalog/forecasts/models/model_items/flare_ler.json
deleted file mode 100644
index ecc90ed9ba..0000000000
--- a/catalog/forecasts/models/model_items/flare_ler.json
+++ /dev/null
@@ -1,171 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flare_ler",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flare_ler"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flare_ler"
-    },
-    {
-      "rel": "self",
-      "href": "flare_ler.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flare_ler.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flare_ler.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=flare_ler?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=flare_ler?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/flare_ler_baselines.json b/catalog/forecasts/models/model_items/flare_ler_baselines.json
deleted file mode 100644
index acac2cfb65..0000000000
--- a/catalog/forecasts/models/model_items/flare_ler_baselines.json
+++ /dev/null
@@ -1,167 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flare_ler_baselines",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-82.0177, 29.6878]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, SUGG\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flare_ler_baselines"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flare_ler_baselines"
-    },
-    {
-      "rel": "self",
-      "href": "flare_ler_baselines.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flare_ler_baselines.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flare_ler_baselines.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=flare_ler_baselines?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=flare_ler_baselines?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/lasso.json b/catalog/forecasts/models/model_items/lasso.json
deleted file mode 100644
index 79b746f2f3..0000000000
--- a/catalog/forecasts/models/model_items/lasso.json
+++ /dev/null
@@ -1,219 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "lasso",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Net_ecosystem_exchange",
-      "Daily latent_heat_flux"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "lasso"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "lasso"
-    },
-    {
-      "rel": "self",
-      "href": "lasso.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/lasso.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/lasso.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/mean.json b/catalog/forecasts/models/model_items/mean.json
deleted file mode 100644
index 8594212ab3..0000000000
--- a/catalog/forecasts/models/model_items/mean.json
+++ /dev/null
@@ -1,219 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "mean",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-78.0418, 39.0337],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-81.9934, 29.6893],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-80.5248, 37.3783],
-      [-100.9154, 46.7697],
-      [-122.3303, 45.7624],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-121.9519, 45.8205],
-      [-145.7514, 63.8811],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-87.8039, 32.5417],
-      [-78.1395, 38.8929],
-      [-110.5391, 44.9535],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-72.1727, 42.5369],
-      [-99.0588, 35.4106]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-20",
-    "end_datetime": "2024-12-09",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Weekly beetle_community_richness",
-      "Weekly beetle_community_abundance"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "mean"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "mean"
-    },
-    {
-      "rel": "self",
-      "href": "mean.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/mean.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/mean.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=mean?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=mean?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=mean?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=mean?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/null.json b/catalog/forecasts/models/model_items/null.json
deleted file mode 100644
index 3085a22774..0000000000
--- a/catalog/forecasts/models/model_items/null.json
+++ /dev/null
@@ -1,206 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "null",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-10-10",
-    "end_datetime": "2024-10-08",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "null"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "null"
-    },
-    {
-      "rel": "self",
-      "href": "null.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/null.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/null.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=null?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=null?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=null?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=null?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/persistenceRW.json b/catalog/forecasts/models/model_items/persistenceRW.json
index 534169080f..8374faa30a 100644
--- a/catalog/forecasts/models/model_items/persistenceRW.json
+++ b/catalog/forecasts/models/model_items/persistenceRW.json
@@ -7,102 +7,31 @@
   "id": "persistenceRW",
   "bbox": [
     [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
+      -122.6692,
+      45.5175,
+      -74.7781,
+      45.5175
     ]
   ],
   "geometry": {
     "type": "MultiPoint",
     "coordinates": [
-      [-149.6106, 68.6307],
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-111.5081, 33.751],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-96.6038, 39.1051],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719]
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      []
     ]
   },
   "properties": {
-    "description": ["\nmodel info: Random walk from the fable package with ensembles used to represent uncertainty\n\nSites: TOOK, BARC, BLWA, CRAM, FLNT, LIRO, SJER, SOAP, SRER, STEI, STER, TALL, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, HEAL, JERC, JORN, KONA, KONZ, OSBS, PUUM, RMNP, SCBI, SERC, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, OAES, ONAQ, ORNL, UNDE, WOOD, WREF, YELL, TEAK, TOOL, TREE, UKFS, DSNY, GRSM, GUAN, HARV, TECR, TOMB, WALK, WLOU, SYCA, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, LECO, LEWI, MART, PRLA, PRPO, REDB, SUGG, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, KING, GUIL, HOPB\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Red_chromatic_coordinate, Daily Dissolved_oxygen, Daily Water_temperature", "\nmodel info: NA\n\nSites: TOOK, BARC, BLWA, CRAM, FLNT, LIRO, SJER, SOAP, SRER, STEI, STER, TALL, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, HEAL, JERC, JORN, KONA, KONZ, OSBS, PUUM, RMNP, SCBI, SERC, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, OAES, ONAQ, ORNL, UNDE, WOOD, WREF, YELL, TEAK, TOOL, TREE, UKFS, DSNY, GRSM, GUAN, HARV, TECR, TOMB, WALK, WLOU, SYCA, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, LECO, LEWI, MART, PRLA, PRPO, REDB, SUGG, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, KING, GUIL, HOPB\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Red_chromatic_coordinate, Daily Dissolved_oxygen, Daily Water_temperature"],
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2024-01-20",
+    "description": "\nmodel info: Random walk model based on most recent stream chl-a observations using the fable::RW() model.\n\nSites: USGS-01427510, USGS-01463500, USGS-05543010, USGS-05549500, USGS-05553700, USGS-05558300, USGS-05586300, USGS-14181500, USGS-14211010, USGS-14211720\n\nVariables: Daily Chlorophyll_a",
+    "start_datetime": "2024-02-07",
+    "end_datetime": "2024-03-13",
     "providers": [
       {
         "url": "pending",
@@ -124,13 +53,8 @@
     "license": "CC0-1.0",
     "keywords": [
       "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily Red_chromatic_coordinate",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature"
+      "usgsrc4cast",
+      "Daily Chlorophyll_a"
     ],
     "table:columns": [
       {
@@ -244,38 +168,8 @@
     "3": {
       "type": "application/x-parquet",
       "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "href": "s3://anonymous@bio230014-bucket01/challenges/forecastsproject_id=/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
+      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecastsproject_id=/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     }
   }
 }
diff --git a/catalog/forecasts/models/model_items/procBlanchardMonod.json b/catalog/forecasts/models/model_items/procBlanchardMonod.json
deleted file mode 100644
index fdb28a80e0..0000000000
--- a/catalog/forecasts/models/model_items/procBlanchardMonod.json
+++ /dev/null
@@ -1,172 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procBlanchardMonod",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procBlanchardMonod"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procBlanchardMonod"
-    },
-    {
-      "rel": "self",
-      "href": "procBlanchardMonod.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procBlanchardMonod.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procBlanchardMonod.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=procBlanchardMonod?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=procBlanchardMonod?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/procBlanchardSteele.json b/catalog/forecasts/models/model_items/procBlanchardSteele.json
deleted file mode 100644
index 48ef7de42b..0000000000
--- a/catalog/forecasts/models/model_items/procBlanchardSteele.json
+++ /dev/null
@@ -1,172 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procBlanchardSteele",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procBlanchardSteele"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procBlanchardSteele"
-    },
-    {
-      "rel": "self",
-      "href": "procBlanchardSteele.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procBlanchardSteele.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procBlanchardSteele.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=procBlanchardSteele?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=procBlanchardSteele?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/procCTMIMonod.json b/catalog/forecasts/models/model_items/procCTMIMonod.json
deleted file mode 100644
index 27ff0117c2..0000000000
--- a/catalog/forecasts/models/model_items/procCTMIMonod.json
+++ /dev/null
@@ -1,172 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procCTMIMonod",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procCTMIMonod"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procCTMIMonod"
-    },
-    {
-      "rel": "self",
-      "href": "procCTMIMonod.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procCTMIMonod.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procCTMIMonod.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=procCTMIMonod?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=procCTMIMonod?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/procCTMISteele.json b/catalog/forecasts/models/model_items/procCTMISteele.json
deleted file mode 100644
index 9e35e32c3e..0000000000
--- a/catalog/forecasts/models/model_items/procCTMISteele.json
+++ /dev/null
@@ -1,172 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procCTMISteele",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procCTMISteele"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procCTMISteele"
-    },
-    {
-      "rel": "self",
-      "href": "procCTMISteele.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procCTMISteele.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procCTMISteele.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=procCTMISteele?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=procCTMISteele?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/procEppleyNorbergMonod.json b/catalog/forecasts/models/model_items/procEppleyNorbergMonod.json
deleted file mode 100644
index 7a6463d29a..0000000000
--- a/catalog/forecasts/models/model_items/procEppleyNorbergMonod.json
+++ /dev/null
@@ -1,172 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procEppleyNorbergMonod",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procEppleyNorbergMonod"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procEppleyNorbergMonod"
-    },
-    {
-      "rel": "self",
-      "href": "procEppleyNorbergMonod.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procEppleyNorbergMonod.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procEppleyNorbergMonod.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=procEppleyNorbergMonod?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=procEppleyNorbergMonod?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/procEppleyNorbergSteele.json b/catalog/forecasts/models/model_items/procEppleyNorbergSteele.json
deleted file mode 100644
index 572265b574..0000000000
--- a/catalog/forecasts/models/model_items/procEppleyNorbergSteele.json
+++ /dev/null
@@ -1,172 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procEppleyNorbergSteele",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procEppleyNorbergSteele"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procEppleyNorbergSteele"
-    },
-    {
-      "rel": "self",
-      "href": "procEppleyNorbergSteele.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procEppleyNorbergSteele.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procEppleyNorbergSteele.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=procEppleyNorbergSteele?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=procEppleyNorbergSteele?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/procHinshelwoodMonod.json b/catalog/forecasts/models/model_items/procHinshelwoodMonod.json
deleted file mode 100644
index 0f0b3e4450..0000000000
--- a/catalog/forecasts/models/model_items/procHinshelwoodMonod.json
+++ /dev/null
@@ -1,172 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procHinshelwoodMonod",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procHinshelwoodMonod"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procHinshelwoodMonod"
-    },
-    {
-      "rel": "self",
-      "href": "procHinshelwoodMonod.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procHinshelwoodMonod.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procHinshelwoodMonod.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=procHinshelwoodMonod?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=procHinshelwoodMonod?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/procHinshelwoodSteele.json b/catalog/forecasts/models/model_items/procHinshelwoodSteele.json
deleted file mode 100644
index 353531e4a0..0000000000
--- a/catalog/forecasts/models/model_items/procHinshelwoodSteele.json
+++ /dev/null
@@ -1,172 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procHinshelwoodSteele",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procHinshelwoodSteele"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procHinshelwoodSteele"
-    },
-    {
-      "rel": "self",
-      "href": "procHinshelwoodSteele.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procHinshelwoodSteele.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procHinshelwoodSteele.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=procHinshelwoodSteele?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=procHinshelwoodSteele?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/prophet_clim_ensemble.json b/catalog/forecasts/models/model_items/prophet_clim_ensemble.json
deleted file mode 100644
index 25f454ce1a..0000000000
--- a/catalog/forecasts/models/model_items/prophet_clim_ensemble.json
+++ /dev/null
@@ -1,207 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "prophet_clim_ensemble",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-119.006, 37.0058],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-122.3303, 45.7624],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2024-01-14",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Red_chromatic_coordinate"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "prophet_clim_ensemble"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "prophet_clim_ensemble"
-    },
-    {
-      "rel": "self",
-      "href": "prophet_clim_ensemble.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/prophet_clim_ensemble.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/prophet_clim_ensemble.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=prophet_clim_ensemble?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=prophet_clim_ensemble?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/randfor.json b/catalog/forecasts/models/model_items/randfor.json
deleted file mode 100644
index 940119d711..0000000000
--- a/catalog/forecasts/models/model_items/randfor.json
+++ /dev/null
@@ -1,212 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "randfor",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily latent_heat_flux"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "randfor"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "randfor"
-    },
-    {
-      "rel": "self",
-      "href": "randfor.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/randfor.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/randfor.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/tg_arima.json b/catalog/forecasts/models/model_items/tg_arima.json
deleted file mode 100644
index b36b00628a..0000000000
--- a/catalog/forecasts/models/model_items/tg_arima.json
+++ /dev/null
@@ -1,309 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_arima",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily latent_heat_flux, Daily Red_chromatic_coordinate, Daily Dissolved_oxygen, Daily Water_temperature, Weekly beetle_community_richness, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population",
-    "start_datetime": "2023-01-01",
-    "end_datetime": "2024-12-09",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily latent_heat_flux",
-      "Daily Red_chromatic_coordinate",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature",
-      "Weekly beetle_community_richness",
-      "Weekly beetle_community_abundance",
-      "Weekly Amblyomma_americanum_population"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_arima"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_arima"
-    },
-    {
-      "rel": "self",
-      "href": "tg_arima.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_arima.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_arima.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/tg_auto_adam.json b/catalog/forecasts/models/model_items/tg_auto_adam.json
deleted file mode 100644
index 2d840c1cfb..0000000000
--- a/catalog/forecasts/models/model_items/tg_auto_adam.json
+++ /dev/null
@@ -1,309 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_auto_adam",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-01-01",
-    "end_datetime": "2023-12-26",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily latent_heat_flux",
-      "Weekly beetle_community_abundance",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Weekly beetle_community_richness",
-      "Daily Chlorophyll_a",
-      "Weekly Amblyomma_americanum_population",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_auto_adam"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_auto_adam"
-    },
-    {
-      "rel": "self",
-      "href": "tg_auto_adam.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_auto_adam.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_auto_adam.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/tg_bag_mlp.json b/catalog/forecasts/models/model_items/tg_bag_mlp.json
deleted file mode 100644
index 6a6dc75986..0000000000
--- a/catalog/forecasts/models/model_items/tg_bag_mlp.json
+++ /dev/null
@@ -1,288 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_bag_mlp",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily latent_heat_flux",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature",
-      "Daily Dissolved_oxygen"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_bag_mlp"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_bag_mlp"
-    },
-    {
-      "rel": "self",
-      "href": "tg_bag_mlp.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_bag_mlp.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_bag_mlp.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/tg_ets.json b/catalog/forecasts/models/model_items/tg_ets.json
deleted file mode 100644
index 8c78c1a21b..0000000000
--- a/catalog/forecasts/models/model_items/tg_ets.json
+++ /dev/null
@@ -1,309 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_ets",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily latent_heat_flux, Daily Red_chromatic_coordinate, Daily Dissolved_oxygen, Daily Water_temperature, Weekly beetle_community_richness, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population",
-    "start_datetime": "2023-01-01",
-    "end_datetime": "2024-12-09",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily latent_heat_flux",
-      "Daily Red_chromatic_coordinate",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature",
-      "Weekly beetle_community_richness",
-      "Weekly beetle_community_abundance",
-      "Weekly Amblyomma_americanum_population"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_ets"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_ets"
-    },
-    {
-      "rel": "self",
-      "href": "tg_ets.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_ets.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_ets.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/tg_humidity_lm.json b/catalog/forecasts/models/model_items/tg_humidity_lm.json
deleted file mode 100644
index 0213eae448..0000000000
--- a/catalog/forecasts/models/model_items/tg_humidity_lm.json
+++ /dev/null
@@ -1,309 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_humidity_lm",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SYCA, TECR, WALK, WLOU, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Water_temperature, Weekly beetle_community_abundance, Daily Red_chromatic_coordinate, Daily latent_heat_flux, Weekly Amblyomma_americanum_population, Weekly beetle_community_richness",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-20",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature",
-      "Weekly beetle_community_abundance",
-      "Daily Red_chromatic_coordinate",
-      "Daily latent_heat_flux",
-      "Weekly Amblyomma_americanum_population",
-      "Weekly beetle_community_richness"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_humidity_lm"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_humidity_lm"
-    },
-    {
-      "rel": "self",
-      "href": "tg_humidity_lm.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_humidity_lm.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_humidity_lm.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/tg_humidity_lm_all_sites.json b/catalog/forecasts/models/model_items/tg_humidity_lm_all_sites.json
deleted file mode 100644
index 81ae8248ea..0000000000
--- a/catalog/forecasts/models/model_items/tg_humidity_lm_all_sites.json
+++ /dev/null
@@ -1,309 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_humidity_lm_all_sites",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR\n\nVariables: Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Weekly Amblyomma_americanum_population, Daily Water_temperature, Daily Chlorophyll_a, Daily Red_chromatic_coordinate, Weekly beetle_community_richness, Weekly beetle_community_abundance",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-20",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Green_chromatic_coordinate",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "Daily Dissolved_oxygen",
-      "Weekly Amblyomma_americanum_population",
-      "Daily Water_temperature",
-      "Daily Chlorophyll_a",
-      "Daily Red_chromatic_coordinate",
-      "Weekly beetle_community_richness",
-      "Weekly beetle_community_abundance"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_humidity_lm_all_sites"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_humidity_lm_all_sites"
-    },
-    {
-      "rel": "self",
-      "href": "tg_humidity_lm_all_sites.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_humidity_lm_all_sites.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_humidity_lm_all_sites.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/tg_lasso.json b/catalog/forecasts/models/model_items/tg_lasso.json
deleted file mode 100644
index 304704a7f4..0000000000
--- a/catalog/forecasts/models/model_items/tg_lasso.json
+++ /dev/null
@@ -1,295 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_lasso",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Dissolved_oxygen, Weekly beetle_community_abundance, Daily Water_temperature, Daily Red_chromatic_coordinate, Weekly beetle_community_richness, Weekly Amblyomma_americanum_population",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-20",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Dissolved_oxygen",
-      "Weekly beetle_community_abundance",
-      "Daily Water_temperature",
-      "Daily Red_chromatic_coordinate",
-      "Weekly beetle_community_richness",
-      "Weekly Amblyomma_americanum_population"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_lasso"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_lasso"
-    },
-    {
-      "rel": "self",
-      "href": "tg_lasso.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_lasso.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_lasso.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/tg_lasso_all_sites.json b/catalog/forecasts/models/model_items/tg_lasso_all_sites.json
deleted file mode 100644
index 212cc80b1e..0000000000
--- a/catalog/forecasts/models/model_items/tg_lasso_all_sites.json
+++ /dev/null
@@ -1,295 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_lasso_all_sites",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature",
-      "Daily Red_chromatic_coordinate",
-      "Weekly beetle_community_abundance",
-      "Weekly Amblyomma_americanum_population",
-      "Weekly beetle_community_richness"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_lasso_all_sites"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_lasso_all_sites"
-    },
-    {
-      "rel": "self",
-      "href": "tg_lasso_all_sites.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_lasso_all_sites.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_lasso_all_sites.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/tg_precip_lm.json b/catalog/forecasts/models/model_items/tg_precip_lm.json
deleted file mode 100644
index 04dc5a0387..0000000000
--- a/catalog/forecasts/models/model_items/tg_precip_lm.json
+++ /dev/null
@@ -1,309 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_precip_lm",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
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-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
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-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
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-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-111.5081, 33.751],
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-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
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-      [-110.5871, 44.9501],
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-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, SYCA, TECR, WALK, WLOU, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily latent_heat_flux, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population, Daily Dissolved_oxygen, Daily Water_temperature, Daily Red_chromatic_coordinate, Weekly beetle_community_richness",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-20",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily latent_heat_flux",
-      "Weekly beetle_community_abundance",
-      "Weekly Amblyomma_americanum_population",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature",
-      "Daily Red_chromatic_coordinate",
-      "Weekly beetle_community_richness"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_precip_lm"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_precip_lm"
-    },
-    {
-      "rel": "self",
-      "href": "tg_precip_lm.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_precip_lm.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_precip_lm.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/tg_precip_lm_all_sites.json b/catalog/forecasts/models/model_items/tg_precip_lm_all_sites.json
deleted file mode 100644
index a765064bd5..0000000000
--- a/catalog/forecasts/models/model_items/tg_precip_lm_all_sites.json
+++ /dev/null
@@ -1,309 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_precip_lm_all_sites",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, WALK, WLOU, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR\n\nVariables: Daily Chlorophyll_a, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Green_chromatic_coordinate, Weekly beetle_community_richness, Daily Dissolved_oxygen, Daily Water_temperature, Daily Red_chromatic_coordinate, Weekly Amblyomma_americanum_population, Weekly beetle_community_abundance",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-20",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "Daily Green_chromatic_coordinate",
-      "Weekly beetle_community_richness",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature",
-      "Daily Red_chromatic_coordinate",
-      "Weekly Amblyomma_americanum_population",
-      "Weekly beetle_community_abundance"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_precip_lm_all_sites"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_precip_lm_all_sites"
-    },
-    {
-      "rel": "self",
-      "href": "tg_precip_lm_all_sites.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_precip_lm_all_sites.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_precip_lm_all_sites.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/tg_randfor.json b/catalog/forecasts/models/model_items/tg_randfor.json
deleted file mode 100644
index 36615acde7..0000000000
--- a/catalog/forecasts/models/model_items/tg_randfor.json
+++ /dev/null
@@ -1,309 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_randfor",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Green_chromatic_coordinate, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population, Daily Dissolved_oxygen, Daily Water_temperature, Daily Red_chromatic_coordinate, Weekly beetle_community_richness",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "Daily Green_chromatic_coordinate",
-      "Weekly beetle_community_abundance",
-      "Weekly Amblyomma_americanum_population",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature",
-      "Daily Red_chromatic_coordinate",
-      "Weekly beetle_community_richness"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_randfor"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_randfor"
-    },
-    {
-      "rel": "self",
-      "href": "tg_randfor.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_randfor.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_randfor.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/tg_randfor_all_sites.json b/catalog/forecasts/models/model_items/tg_randfor_all_sites.json
deleted file mode 100644
index f3e3a7da28..0000000000
--- a/catalog/forecasts/models/model_items/tg_randfor_all_sites.json
+++ /dev/null
@@ -1,281 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_randfor_all_sites",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Weekly beetle_community_richness",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature",
-      "Daily Red_chromatic_coordinate",
-      "Weekly Amblyomma_americanum_population",
-      "Weekly beetle_community_abundance"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_randfor_all_sites"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_randfor_all_sites"
-    },
-    {
-      "rel": "self",
-      "href": "tg_randfor_all_sites.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_randfor_all_sites.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_randfor_all_sites.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/tg_tbats.json b/catalog/forecasts/models/model_items/tg_tbats.json
deleted file mode 100644
index 9dff7561ed..0000000000
--- a/catalog/forecasts/models/model_items/tg_tbats.json
+++ /dev/null
@@ -1,309 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_tbats",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU, ARIK, BIGC, BLDE, BLUE\n\nVariables: Daily Chlorophyll_a, Daily latent_heat_flux, Daily Red_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Green_chromatic_coordinate, Weekly Amblyomma_americanum_population, Weekly beetle_community_richness, Daily Water_temperature, Weekly beetle_community_abundance, Daily Dissolved_oxygen",
-    "start_datetime": "2023-01-01",
-    "end_datetime": "2024-12-09",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily latent_heat_flux",
-      "Daily Red_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily Green_chromatic_coordinate",
-      "Weekly Amblyomma_americanum_population",
-      "Weekly beetle_community_richness",
-      "Daily Water_temperature",
-      "Weekly beetle_community_abundance",
-      "Daily Dissolved_oxygen"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_tbats"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_tbats"
-    },
-    {
-      "rel": "self",
-      "href": "tg_tbats.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_tbats.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_tbats.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/tg_temp_lm.json b/catalog/forecasts/models/model_items/tg_temp_lm.json
deleted file mode 100644
index 16a005b79d..0000000000
--- a/catalog/forecasts/models/model_items/tg_temp_lm.json
+++ /dev/null
@@ -1,309 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_temp_lm",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB\n\nVariables: Daily latent_heat_flux, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Red_chromatic_coordinate, Daily Water_temperature, Daily Dissolved_oxygen, Daily Chlorophyll_a, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population, Weekly beetle_community_richness",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-20",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily latent_heat_flux",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature",
-      "Daily Dissolved_oxygen",
-      "Daily Chlorophyll_a",
-      "Weekly beetle_community_abundance",
-      "Weekly Amblyomma_americanum_population",
-      "Weekly beetle_community_richness"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_temp_lm"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_temp_lm"
-    },
-    {
-      "rel": "self",
-      "href": "tg_temp_lm.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_temp_lm.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_temp_lm.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/tg_temp_lm_all_sites.json b/catalog/forecasts/models/model_items/tg_temp_lm_all_sites.json
deleted file mode 100644
index 199d77394f..0000000000
--- a/catalog/forecasts/models/model_items/tg_temp_lm_all_sites.json
+++ /dev/null
@@ -1,309 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_temp_lm_all_sites",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI\n\nVariables: Daily Chlorophyll_a, Daily latent_heat_flux, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Red_chromatic_coordinate, Daily Water_temperature, Daily Dissolved_oxygen, Weekly Amblyomma_americanum_population, Weekly beetle_community_richness, Weekly beetle_community_abundance",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-20",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily latent_heat_flux",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature",
-      "Daily Dissolved_oxygen",
-      "Weekly Amblyomma_americanum_population",
-      "Weekly beetle_community_richness",
-      "Weekly beetle_community_abundance"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_temp_lm_all_sites"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_temp_lm_all_sites"
-    },
-    {
-      "rel": "self",
-      "href": "tg_temp_lm_all_sites.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_temp_lm_all_sites.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_temp_lm_all_sites.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=richness/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1W/variable=abundance/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/tslm.json b/catalog/forecasts/models/model_items/tslm.json
deleted file mode 100644
index dcf6a6d898..0000000000
--- a/catalog/forecasts/models/model_items/tslm.json
+++ /dev/null
@@ -1,194 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tslm",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-09-15",
-    "end_datetime": "2023-10-20",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature",
-      "Daily Dissolved_oxygen"
-    ],
-    "table:columns": [
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tslm"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tslm"
-    },
-    {
-      "rel": "self",
-      "href": "tslm.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tslm.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tslm.json\")\n\n"
-    },
-    "2": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tslm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=tslm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tslm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=tslm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/forecasts/models/model_items/xgboost_parallel.json b/catalog/forecasts/models/model_items/xgboost_parallel.json
deleted file mode 100644
index 4d33de8063..0000000000
--- a/catalog/forecasts/models/model_items/xgboost_parallel.json
+++ /dev/null
@@ -1,251 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "xgboost_parallel",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-110.5391, 44.9535],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-10-19",
-    "end_datetime": "2023-11-22",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Net_ecosystem_exchange",
-      "Daily latent_heat_flux",
-      "Daily Dissolved_oxygen",
-      "Daily Green_chromatic_coordinate",
-      "Daily Water_temperature",
-      "Daily Red_chromatic_coordinate",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "prediction",
-        "type": "double",
-        "description": "predicted value for variable"
-      },
-      {
-        "name": "parameter",
-        "type": "string",
-        "description": "ensemble member or distribution parameter"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "xgboost_parallel"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "xgboost_parallel"
-    },
-    {
-      "rel": "self",
-      "href": "xgboost_parallel.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/xgboost_parallel.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/xgboost_parallel.json\")\n\n"
-    },
-    "2": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=nee/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=le/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=oxygen/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=gcc_90/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=temperature/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=rcc_90/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/duration=P1D/variable=chla/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/inventory/collection.json b/catalog/inventory/collection.json
index f7d4b1dc4b..daae5522bc 100644
--- a/catalog/inventory/collection.json
+++ b/catalog/inventory/collection.json
@@ -1,6 +1,6 @@
 {
   "id": "inventory",
-  "description": "The catalog contains forecasts for the NEON Ecological Forecasting Challenge. The forecasts are the raw forecasts that include all ensemble members (if a forecast represents uncertainty using an ensemble).  Due to the size of the raw forecasts, we recommend accessing the scores (summaries of the forecasts) to analyze forecasts (unless you need the individual ensemble members). You can access the forecasts at the top level of the dataset where all models, variables, and dates that forecasts were produced (reference_datetime) are available. The code to access the entire dataset is provided as an asset. Given the size of the forecast catalog, it can be time-consuming to access the data at the full dataset level. For quicker access to the forecasts for a particular model (model_id), we also provide the code to access the data at the model_id level as an asset for each model.",
+  "description": "The catalog contains forecasts for the EFI-USGS River Chlorophyll Forecasting Challenge. The forecasts are the raw forecasts that include all ensemble members (if a forecast represents uncertainty using an ensemble).  Due to the size of the raw forecasts, we recommend accessing the scores (summaries of the forecasts) to analyze forecasts (unless you need the individual ensemble members). You can access the forecasts at the top level of the dataset where all models, variables, and dates that forecasts were produced (reference_datetime) are available. The code to access the entire dataset is provided as an asset. Given the size of the forecast catalog, it can be time-consuming to access the data at the full dataset level. For quicker access to the forecasts for a particular model (model_id), we also provide the code to access the data at the model_id level as an asset for each model.",
   "stac_version": "1.0.0",
   "license": "CC0-1.0",
   "stac_extensions": [
@@ -31,14 +31,14 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
@@ -47,18 +47,18 @@
     "spatial": {
       "bbox": [
         [
-          -156.6194,
-          17.9696,
-          -66.7987,
-          71.2824
+          -122.6692,
+          39.6327,
+          -74.7781,
+          45.5175
         ]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2023-01-01T00:00:00Z",
-          "2024-12-09T00:00:00Z"
+          "2024-02-07T00:00:00Z",
+          "2024-03-14T00:00:00Z"
         ]
       ]
     }
@@ -137,30 +137,30 @@
   ],
   "assets": {
     "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/inventory/catalog/forecasts/project_id=neon4cast?endpoint_override=sdsc.osn.xsede.org\"",
+      "href": "s3://anonymous@bio230014-bucket01/challenges/inventory/catalog/forecasts/project_id=usgsrc4cast?endpoint_override=sdsc.osn.xsede.org",
       "type": "application/x-parquet",
       "title": "Forecast Inventory Access",
       "roles": [
         "data"
       ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for the forecast challenge inventory bucket.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/inventory/catalog/forecasts/project_id=neon4cast?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `arrow` for remote access to the database. This R code will return results for the forecast challenge inventory bucket.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/inventory/catalog/forecasts/project_id=usgsrc4cast?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     },
     "data.1": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/inventory/catalog/scores/project_id=neon4cast?endpoint_override=sdsc.osn.xsede.org\"",
+      "href": "s3://anonymous@bio230014-bucket01/challenges/inventory/catalog/scores/project_id=usgsrc4cast?endpoint_override=sdsc.osn.xsede.org",
       "type": "application/x-parquet",
       "title": "Scores Inventory Access",
       "roles": [
         "data"
       ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for the forecast challenge inventory bucket.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/inventory/catalog/scores/project_id=neon4cast?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `arrow` for remote access to the database. This R code will return results for the forecast challenge inventory bucket.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/inventory/catalog/scores/project_id=usgsrc4cast?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     },
     "thumbnail": {
-      "href": "https://raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/thumbnail_plots/neon_forest.jpg",
+      "href": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/thumbnails/image/Streamgaging%20Basics%20photo%20showing%20Acoustic%20Doppler%20Current%20Profiler2.jpg",
       "type": "image/JPEG",
       "roles": [
         "thumbnail"
       ],
-      "title": "NEON Image"
+      "title": "USGS Image"
     }
   }
 }
diff --git a/catalog/inventory/create_inventory_page.R b/catalog/inventory/create_inventory_page.R
index 654aac1097..5e1f461f2a 100644
--- a/catalog/inventory/create_inventory_page.R
+++ b/catalog/inventory/create_inventory_page.R
@@ -33,7 +33,8 @@ inventory_theme_df <- arrow::open_dataset(arrow::s3_bucket(config$inventory_buck
 
 inventory_data_df <- duckdbfs::open_dataset(glue::glue("s3://{config$inventory_bucket}/catalog/forecasts"),
                        s3_endpoint = config$endpoint, anonymous=TRUE) |>
-  collect()
+  collect() |>
+  dplyr::filter(project_id == config$project_id)
 
 theme_models <- inventory_data_df |>
   distinct(model_id)
diff --git a/catalog/model_metadata.R b/catalog/model_metadata.R
index d2e9a5c564..cce5444df6 100644
--- a/catalog/model_metadata.R
+++ b/catalog/model_metadata.R
@@ -171,7 +171,11 @@ for(i in 1:nrow(registered_models)){
   file_name <- paste0(metadata$model_id, ".json")
   jsonlite::write_json(metadata, path = file.path("catalog",file_name), pretty = TRUE)
 
-  minioclient::mc_cp(file.path("catalog",file_name), file.path("osn",config$model_metadata_bucket, file_name))
+  minioclient::mc_cp(file.path("catalog",file_name),
+                     file.path("osn",
+                               config$model_metadata_bucket,
+                               paste0("project_id=", config$project_id),
+                               file_name))
 
   unlink(file.path("catalog",file_name))
 }
diff --git a/catalog/noaa_forecasts/Pseudo/collection.json b/catalog/noaa_forecasts/Pseudo/collection.json
index b14066f4bc..c96b7a837e 100644
--- a/catalog/noaa_forecasts/Pseudo/collection.json
+++ b/catalog/noaa_forecasts/Pseudo/collection.json
@@ -1,6 +1,6 @@
 {
   "id": "Pseudo",
-  "description": "The catalog contains NOAA forecasts used for the NEON Ecological Forecasting Challenge. The forecasts are the raw forecasts that include all ensemble members (if a forecast represents uncertainty using an ensemble). You can access the forecasts at the top level of the dataset where all models, variables, and dates that forecasts were produced (reference_datetime) are available. The code to access the entire dataset is provided as an asset. Given the size of the forecast catalog, it can be time-consuming to access the data at the full dataset level. For quicker access to the forecasts for a site or datetime, we also provide the code to access the data at the site_id and datetime level as an asset for each forecast",
+  "description": "The catalog contains NOAA forecasts used for the EFI-USGS River Chlorophyll Forecasting Challenge. The forecasts are the raw forecasts that include all ensemble members (if a forecast represents uncertainty using an ensemble). You can access the forecasts at the top level of the dataset where all models, variables, and dates that forecasts were produced (reference_datetime) are available. The code to access the entire dataset is provided as an asset. Given the size of the forecast catalog, it can be time-consuming to access the data at the full dataset level. For quicker access to the forecasts for a site or datetime, we also provide the code to access the data at the site_id and datetime level as an asset for each forecast",
   "stac_version": "1.0.0",
   "license": "CC0-1.0",
   "stac_extensions": [
@@ -31,14 +31,14 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
@@ -46,14 +46,14 @@
   "extent": {
     "spatial": {
       "bbox": [
-        [-156.6194, 17.9696, -66.7987, 71.2824]
+        [-122.6692, 39.6328, -74.7781, 45.5175]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2020-09-25T00:00:00Z",
-          "2024-01-20T00:00:00Z"
+          "2024-01-29T00:00:00Z",
+          "2024-03-13T00:00:00Z"
         ]
       ]
     }
@@ -122,21 +122,21 @@
   ],
   "assets": {
     "data": {
-      "href": "\"s3://anonymous@drivers/noaa/gefs-v12-reprocess/pseudo/parquet/0?endpoint_override=s3.flare-forecast.org\"",
+      "href": "s3://anonymous@drivers/noaa/gefs-v12-reprocess/pseudo/parquet?endpoint_override=s3.flare-forecast.org",
       "type": "application/x-parquet",
       "title": "Database Access",
       "roles": [
         "data"
       ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for NEON forecasts associated with the forecasting challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@drivers/noaa/gefs-v12-reprocess/pseudo/parquet/0?endpoint_override=s3.flare-forecast.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `arrow` for remote access to the database. This R code will return results for NEON forecasts associated with the forecasting challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@drivers/noaa/gefs-v12-reprocess/pseudo/parquet?endpoint_override=s3.flare-forecast.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     },
     "thumbnail": {
-      "href": "https://raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/thumbnail_plots/neon_wetland.jpg",
+      "href": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/DSC_0001.jpg",
       "type": "image/JPEG",
       "roles": [
         "thumbnail"
       ],
-      "title": "NEON Image"
+      "title": "USGS Image"
     }
   }
 }
diff --git a/catalog/noaa_forecasts/Stage1-stats/collection.json b/catalog/noaa_forecasts/Stage1-stats/collection.json
index 853172b639..957d2aa360 100644
--- a/catalog/noaa_forecasts/Stage1-stats/collection.json
+++ b/catalog/noaa_forecasts/Stage1-stats/collection.json
@@ -1,6 +1,6 @@
 {
   "id": "Stage1-stats",
-  "description": "The catalog contains NOAA forecasts used for the NEON Ecological Forecasting Challenge. The forecasts are the raw forecasts that include all ensemble members (if a forecast represents uncertainty using an ensemble). You can access the forecasts at the top level of the dataset where all models, variables, and dates that forecasts were produced (reference_datetime) are available. The code to access the entire dataset is provided as an asset. Given the size of the forecast catalog, it can be time-consuming to access the data at the full dataset level. For quicker access to the forecasts for a site or datetime, we also provide the code to access the data at the site_id and datetime level as an asset for each forecast",
+  "description": "The catalog contains NOAA forecasts used for the EFI-USGS River Chlorophyll Forecasting Challenge. The forecasts are the raw forecasts that include all ensemble members (if a forecast represents uncertainty using an ensemble). You can access the forecasts at the top level of the dataset where all models, variables, and dates that forecasts were produced (reference_datetime) are available. The code to access the entire dataset is provided as an asset. Given the size of the forecast catalog, it can be time-consuming to access the data at the full dataset level. For quicker access to the forecasts for a site or datetime, we also provide the code to access the data at the site_id and datetime level as an asset for each forecast",
   "stac_version": "1.0.0",
   "license": "CC0-1.0",
   "stac_extensions": [
@@ -31,14 +31,14 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
@@ -46,14 +46,14 @@
   "extent": {
     "spatial": {
       "bbox": [
-        [-156.6194, 17.9696, -66.7987, 71.2824]
+        [-122.6692, 39.6328, -74.7781, 45.5175]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2020-09-25T00:00:00Z",
-          "2024-01-20T00:00:00Z"
+          "2024-01-29T00:00:00Z",
+          "2024-03-13T00:00:00Z"
         ]
       ]
     }
@@ -122,21 +122,21 @@
   ],
   "assets": {
     "data": {
-      "href": "\"s3://anonymous@drivers/noaa/gefs-v12-reprocess/stage1-stats/parquet/0?endpoint_override=s3.flare-forecast.org\"",
+      "href": "s3://anonymous@drivers/noaa/gefs-v12-reprocess/stage1-stats/parquet?endpoint_override=s3.flare-forecast.org",
       "type": "application/x-parquet",
       "title": "Database Access",
       "roles": [
         "data"
       ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for NEON forecasts associated with the forecasting challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@drivers/noaa/gefs-v12-reprocess/stage1-stats/parquet/0?endpoint_override=s3.flare-forecast.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `arrow` for remote access to the database. This R code will return results for NEON forecasts associated with the forecasting challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@drivers/noaa/gefs-v12-reprocess/stage1-stats/parquet?endpoint_override=s3.flare-forecast.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     },
     "thumbnail": {
-      "href": "https://raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/thumbnail_plots/neon_wetland.jpg",
+      "href": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/DSC_0001.jpg",
       "type": "image/JPEG",
       "roles": [
         "thumbnail"
       ],
-      "title": "NEON Image"
+      "title": "USGS Image"
     }
   }
 }
diff --git a/catalog/noaa_forecasts/Stage1/collection.json b/catalog/noaa_forecasts/Stage1/collection.json
index 3fb11856e4..f7d612e3f2 100644
--- a/catalog/noaa_forecasts/Stage1/collection.json
+++ b/catalog/noaa_forecasts/Stage1/collection.json
@@ -1,6 +1,6 @@
 {
   "id": "Stage1",
-  "description": "The catalog contains NOAA forecasts used for the NEON Ecological Forecasting Challenge. The forecasts are the raw forecasts that include all ensemble members (if a forecast represents uncertainty using an ensemble). You can access the forecasts at the top level of the dataset where all models, variables, and dates that forecasts were produced (reference_datetime) are available. The code to access the entire dataset is provided as an asset. Given the size of the forecast catalog, it can be time-consuming to access the data at the full dataset level. For quicker access to the forecasts for a site or datetime, we also provide the code to access the data at the site_id and datetime level as an asset for each forecast",
+  "description": "The catalog contains NOAA forecasts used for the EFI-USGS River Chlorophyll Forecasting Challenge. The forecasts are the raw forecasts that include all ensemble members (if a forecast represents uncertainty using an ensemble). You can access the forecasts at the top level of the dataset where all models, variables, and dates that forecasts were produced (reference_datetime) are available. The code to access the entire dataset is provided as an asset. Given the size of the forecast catalog, it can be time-consuming to access the data at the full dataset level. For quicker access to the forecasts for a site or datetime, we also provide the code to access the data at the site_id and datetime level as an asset for each forecast",
   "stac_version": "1.0.0",
   "license": "CC0-1.0",
   "stac_extensions": [
@@ -31,14 +31,14 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
@@ -46,14 +46,14 @@
   "extent": {
     "spatial": {
       "bbox": [
-        [-156.6194, 17.9696, -66.7987, 71.2824]
+        [-122.6692, 39.6328, -74.7781, 45.5175]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2020-09-25T00:00:00Z",
-          "2024-01-20T00:00:00Z"
+          "2024-01-29T00:00:00Z",
+          "2024-03-13T00:00:00Z"
         ]
       ]
     }
@@ -122,21 +122,21 @@
   ],
   "assets": {
     "data": {
-      "href": "\"s3://anonymous@drivers/noaa/gefs-v12-reprocess/stage1/parquet/0?endpoint_override=s3.flare-forecast.org\"",
+      "href": "s3://anonymous@drivers/noaa/gefs-v12-reprocess/stage1/parquet?endpoint_override=s3.flare-forecast.org",
       "type": "application/x-parquet",
       "title": "Database Access",
       "roles": [
         "data"
       ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for NEON forecasts associated with the forecasting challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@drivers/noaa/gefs-v12-reprocess/stage1/parquet/0?endpoint_override=s3.flare-forecast.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `arrow` for remote access to the database. This R code will return results for NEON forecasts associated with the forecasting challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@drivers/noaa/gefs-v12-reprocess/stage1/parquet?endpoint_override=s3.flare-forecast.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     },
     "thumbnail": {
-      "href": "https://raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/thumbnail_plots/neon_wetland.jpg",
+      "href": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/DSC_0001.jpg",
       "type": "image/JPEG",
       "roles": [
         "thumbnail"
       ],
-      "title": "NEON Image"
+      "title": "USGS Image"
     }
   }
 }
diff --git a/catalog/noaa_forecasts/Stage2/collection.json b/catalog/noaa_forecasts/Stage2/collection.json
index 13d09aa428..0557c9890d 100644
--- a/catalog/noaa_forecasts/Stage2/collection.json
+++ b/catalog/noaa_forecasts/Stage2/collection.json
@@ -1,6 +1,6 @@
 {
   "id": "Stage2",
-  "description": "The catalog contains NOAA forecasts used for the NEON Ecological Forecasting Challenge. The forecasts are the raw forecasts that include all ensemble members (if a forecast represents uncertainty using an ensemble). You can access the forecasts at the top level of the dataset where all models, variables, and dates that forecasts were produced (reference_datetime) are available. The code to access the entire dataset is provided as an asset. Given the size of the forecast catalog, it can be time-consuming to access the data at the full dataset level. For quicker access to the forecasts for a site or datetime, we also provide the code to access the data at the site_id and datetime level as an asset for each forecast",
+  "description": "The catalog contains NOAA forecasts used for the EFI-USGS River Chlorophyll Forecasting Challenge. The forecasts are the raw forecasts that include all ensemble members (if a forecast represents uncertainty using an ensemble). You can access the forecasts at the top level of the dataset where all models, variables, and dates that forecasts were produced (reference_datetime) are available. The code to access the entire dataset is provided as an asset. Given the size of the forecast catalog, it can be time-consuming to access the data at the full dataset level. For quicker access to the forecasts for a site or datetime, we also provide the code to access the data at the site_id and datetime level as an asset for each forecast",
   "stac_version": "1.0.0",
   "license": "CC0-1.0",
   "stac_extensions": [
@@ -31,14 +31,14 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
@@ -46,14 +46,14 @@
   "extent": {
     "spatial": {
       "bbox": [
-        [-156.6194, 17.9696, -66.7987, 71.2824]
+        [-122.6692, 39.6328, -74.7781, 45.5175]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2020-09-25T00:00:00Z",
-          "2024-01-20T00:00:00Z"
+          "2024-01-29T00:00:00Z",
+          "2024-03-13T00:00:00Z"
         ]
       ]
     }
@@ -122,21 +122,21 @@
   ],
   "assets": {
     "data": {
-      "href": "\"s3://anonymous@drivers/noaa/gefs-v12-reprocess/stage2/parquet/0?endpoint_override=s3.flare-forecast.org\"",
+      "href": "s3://anonymous@drivers/noaa/gefs-v12-reprocess/stage2/parquet?endpoint_override=s3.flare-forecast.org",
       "type": "application/x-parquet",
       "title": "Database Access",
       "roles": [
         "data"
       ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for NEON forecasts associated with the forecasting challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@drivers/noaa/gefs-v12-reprocess/stage2/parquet/0?endpoint_override=s3.flare-forecast.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `arrow` for remote access to the database. This R code will return results for NEON forecasts associated with the forecasting challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@drivers/noaa/gefs-v12-reprocess/stage2/parquet?endpoint_override=s3.flare-forecast.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     },
     "thumbnail": {
-      "href": "https://raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/thumbnail_plots/neon_wetland.jpg",
+      "href": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/DSC_0001.jpg",
       "type": "image/JPEG",
       "roles": [
         "thumbnail"
       ],
-      "title": "NEON Image"
+      "title": "USGS Image"
     }
   }
 }
diff --git a/catalog/noaa_forecasts/Stage3/collection.json b/catalog/noaa_forecasts/Stage3/collection.json
index 93f73dac9f..9946239a4c 100644
--- a/catalog/noaa_forecasts/Stage3/collection.json
+++ b/catalog/noaa_forecasts/Stage3/collection.json
@@ -1,6 +1,6 @@
 {
   "id": "Stage3",
-  "description": "The catalog contains NOAA forecasts used for the NEON Ecological Forecasting Challenge. The forecasts are the raw forecasts that include all ensemble members (if a forecast represents uncertainty using an ensemble). You can access the forecasts at the top level of the dataset where all models, variables, and dates that forecasts were produced (reference_datetime) are available. The code to access the entire dataset is provided as an asset. Given the size of the forecast catalog, it can be time-consuming to access the data at the full dataset level. For quicker access to the forecasts for a site or datetime, we also provide the code to access the data at the site_id and datetime level as an asset for each forecast",
+  "description": "The catalog contains NOAA forecasts used for the EFI-USGS River Chlorophyll Forecasting Challenge. The forecasts are the raw forecasts that include all ensemble members (if a forecast represents uncertainty using an ensemble). You can access the forecasts at the top level of the dataset where all models, variables, and dates that forecasts were produced (reference_datetime) are available. The code to access the entire dataset is provided as an asset. Given the size of the forecast catalog, it can be time-consuming to access the data at the full dataset level. For quicker access to the forecasts for a site or datetime, we also provide the code to access the data at the site_id and datetime level as an asset for each forecast",
   "stac_version": "1.0.0",
   "license": "CC0-1.0",
   "stac_extensions": [
@@ -31,14 +31,14 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
@@ -46,14 +46,14 @@
   "extent": {
     "spatial": {
       "bbox": [
-        [-156.6194, 17.9696, -66.7987, 71.2824]
+        [-122.6692, 39.6328, -74.7781, 45.5175]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2020-09-25T00:00:00Z",
-          "2024-01-20T00:00:00Z"
+          "2024-01-29T00:00:00Z",
+          "2024-03-13T00:00:00Z"
         ]
       ]
     }
@@ -122,21 +122,21 @@
   ],
   "assets": {
     "data": {
-      "href": "\"s3://anonymous@drivers/noaa/gefs-v12-reprocess/stage3/parquet/0?endpoint_override=s3.flare-forecast.org\"",
+      "href": "s3://anonymous@drivers/noaa/gefs-v12-reprocess/stage3/parquet?endpoint_override=s3.flare-forecast.org",
       "type": "application/x-parquet",
       "title": "Database Access",
       "roles": [
         "data"
       ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for NEON forecasts associated with the forecasting challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@drivers/noaa/gefs-v12-reprocess/stage3/parquet/0?endpoint_override=s3.flare-forecast.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `arrow` for remote access to the database. This R code will return results for NEON forecasts associated with the forecasting challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@drivers/noaa/gefs-v12-reprocess/stage3/parquet?endpoint_override=s3.flare-forecast.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     },
     "thumbnail": {
-      "href": "https://raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/thumbnail_plots/neon_wetland.jpg",
+      "href": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/DSC_0001.jpg",
       "type": "image/JPEG",
       "roles": [
         "thumbnail"
       ],
-      "title": "NEON Image"
+      "title": "USGS Image"
     }
   }
 }
diff --git a/catalog/noaa_forecasts/collection.json b/catalog/noaa_forecasts/collection.json
index e0b374eddb..cb74bce851 100644
--- a/catalog/noaa_forecasts/collection.json
+++ b/catalog/noaa_forecasts/collection.json
@@ -1,6 +1,6 @@
 {
   "id": "noaa-forecasts",
-  "description": "The catalog contains NOAA forecasts used for the NEON Ecological Forecasting Challenge. The forecasts are the raw forecasts that include all ensemble members (if a forecast represents uncertainty using an ensemble). You can access the forecasts at the top level of the dataset where all models, variables, and dates that forecasts were produced (reference_datetime) are available. The code to access the entire dataset is provided as an asset. Given the size of the forecast catalog, it can be time-consuming to access the data at the full dataset level. For quicker access to the forecasts for a site or datetime, we also provide the code to access the data at the site_id and datetime level as an asset for each forecast",
+  "description": "The catalog contains NOAA forecasts used for the EFI-USGS River Chlorophyll Forecasting Challenge. The forecasts are the raw forecasts that include all ensemble members (if a forecast represents uncertainty using an ensemble). You can access the forecasts at the top level of the dataset where all models, variables, and dates that forecasts were produced (reference_datetime) are available. The code to access the entire dataset is provided as an asset. Given the size of the forecast catalog, it can be time-consuming to access the data at the full dataset level. For quicker access to the forecasts for a site or datetime, we also provide the code to access the data at the site_id and datetime level as an asset for each forecast",
   "stac_version": "1.0.0",
   "license": "CC0-1.0",
   "stac_extensions": [
@@ -61,14 +61,14 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
@@ -77,18 +77,18 @@
     "spatial": {
       "bbox": [
         [
-          -156.6194,
-          17.9696,
-          -66.7987,
-          71.2824
+          -122.6692,
+          39.6327,
+          -74.7781,
+          45.5175
         ]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2020-09-25T00:00:00Z",
-          "2024-01-20T00:00:00Z"
+          "2024-01-29T00:00:00Z",
+          "2024-03-13T00:00:00Z"
         ]
       ]
     }
@@ -157,21 +157,21 @@
   ],
   "assets": {
     "data": {
-      "href": "s3://anonymous@drivers/noaa/gefs-v12-reprocess/?endpoint_override=s3.flare-forecast.org",
+      "href": "s3://anonymous@drivers/noaa/gefs-v12-reprocess/?endpoint_override=sdsc.osn.xsede.org",
       "type": "application/x-parquet",
       "title": "Database Access",
       "roles": [
         "data"
       ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for the VERA Forecasting Challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@drivers/noaa/gefs-v12-reprocess/?endpoint_override=s3.flare-forecast.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `arrow` for remote access to the database. This R code will return results for the Forecasting Challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@drivers/noaa/gefs-v12-reprocess/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     },
     "thumbnail": {
-      "href": "https://raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/thumbnail_plots/neon_wetland.jpg",
+      "href": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/DSC_0001.jpg",
       "type": "image/JPEG",
       "roles": [
         "thumbnail"
       ],
-      "title": "NEON Image"
+      "title": "USGS Image"
     }
   }
 }
diff --git a/catalog/noaa_forecasts/noaa_forecasts.R b/catalog/noaa_forecasts/noaa_forecasts.R
index 47f473ece4..6fbdeab7f3 100644
--- a/catalog/noaa_forecasts/noaa_forecasts.R
+++ b/catalog/noaa_forecasts/noaa_forecasts.R
@@ -29,7 +29,9 @@ noaa_description_create <- data.frame(site_id = 'For forecasts that are not on a
 noaa_theme_df <- arrow::open_dataset(arrow::s3_bucket(paste0(config$noaa_forecast_bucket,"stage2/parquet/0/2023-08-01/feea"), endpoint_override = config$noaa_endpoint, anonymous = TRUE))
 
 
-noaa_theme_dates <- arrow::open_dataset(arrow::s3_bucket(paste0(config$noaa_forecast_bucket,"stage2/parquet/"), endpoint_override = config$noaa_endpoint, anonymous = TRUE)) |>
+noaa_theme_dates <- arrow::open_dataset(arrow::s3_bucket(paste0(config$driver_bucket,"/gefs-v12/stage2"),
+                                                         endpoint_override = config$endpoint,
+                                                         anonymous = TRUE)) |>
   dplyr::summarise(min(datetime),max(datetime)) |>
   collect()
 noaa_min_date <- noaa_theme_dates$`min(datetime)`
@@ -75,7 +77,7 @@ stac4cast::build_forecast_scores(table_schema = noaa_theme_df,
 ## BUILD VARIABLE GROUPS
 ## find group sites
 find_noaa_sites <- read_csv(config$site_table) |>
-  distinct(field_site_id)
+  distinct(site_id)
 
 for (i in 1:length(config$noaa_forecast_groups)){ ## organize variable groups
   print(config$noaa_forecast_groups[i])
diff --git a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/collection.json b/catalog/scores/Aquatics/Daily_Dissolved_oxygen/collection.json
deleted file mode 100644
index f522fa4259..0000000000
--- a/catalog/scores/Aquatics/Daily_Dissolved_oxygen/collection.json
+++ /dev/null
@@ -1,292 +0,0 @@
-{
-  "id": "Daily_Dissolved_oxygen",
-  "description": "This page includes all models for the Daily_Dissolved_oxygen variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/GLEON_lm_lag_1day.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/air2waterSat_2.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_f1.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_prophet.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/climatology.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/null.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/persistenceRW.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_bag_mlp.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Daily_Dissolved_oxygen",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-149.6106, 18.1135, -66.7987, 68.6698]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-01T00:00:00Z",
-          "2023-11-30T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "observation",
-      "type": "double",
-      "description": "observed value for variable"
-    },
-    {
-      "name": "crps",
-      "type": "double",
-      "description": "crps forecast score"
-    },
-    {
-      "name": "logs",
-      "type": "double",
-      "description": "logs forecast score"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique project identifier"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "date",
-      "type": "string",
-      "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=P1D/variable=oxygen?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=P1D/variable=oxygen?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/scores/Aquatics/Daily_Water_temperature/collection.json b/catalog/scores/Aquatics/Daily_Water_temperature/collection.json
deleted file mode 100644
index 7badca14de..0000000000
--- a/catalog/scores/Aquatics/Daily_Water_temperature/collection.json
+++ /dev/null
@@ -1,362 +0,0 @@
-{
-  "id": "Daily_Water_temperature",
-  "description": "This page includes all models for the Daily_Water_temperature variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_f1.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_prophet.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/climatology.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/fARIMA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/fARIMA_clim_ensemble.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/fTSLM_lag.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/GLEON_JRabaey_temp_physics.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/GLEON_lm_lag_1day.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/GLEON_physics.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/air2waterSat_2.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/baseline_ensemble.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flareGLM.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flareGLM_noDA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flareGOTM.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flareGOTM_noDA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flareSimstrat.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flareSimstrat_noDA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flare_ler.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flare_ler_baselines.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/null.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/persistenceRW.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_bag_mlp.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Daily_Water_temperature",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-149.6106, 18.1135, -66.7987, 68.6698]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-01T00:00:00Z",
-          "2023-12-15T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "observation",
-      "type": "double",
-      "description": "observed value for variable"
-    },
-    {
-      "name": "crps",
-      "type": "double",
-      "description": "crps forecast score"
-    },
-    {
-      "name": "logs",
-      "type": "double",
-      "description": "logs forecast score"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique project identifier"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "date",
-      "type": "string",
-      "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=P1D/variable=temperature?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=P1D/variable=temperature?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/scores/Beetles/Weekly_beetle_community_abundance/collection.json b/catalog/scores/Beetles/Weekly_beetle_community_abundance/collection.json
deleted file mode 100644
index 6e88a4b2d7..0000000000
--- a/catalog/scores/Beetles/Weekly_beetle_community_abundance/collection.json
+++ /dev/null
@@ -1,202 +0,0 @@
-{
-  "id": "Weekly_beetle_community_abundance",
-  "description": "This page includes all models for the Weekly_beetle_community_abundance variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Weekly_beetle_community_abundance",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-02T00:00:00Z",
-          "2023-10-16T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "observation",
-      "type": "double",
-      "description": "observed value for variable"
-    },
-    {
-      "name": "crps",
-      "type": "double",
-      "description": "crps forecast score"
-    },
-    {
-      "name": "logs",
-      "type": "double",
-      "description": "logs forecast score"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique project identifier"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "date",
-      "type": "string",
-      "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=P1W/variable=abundance?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=P1W/variable=abundance?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/scores/Beetles/Weekly_beetle_community_richness/collection.json b/catalog/scores/Beetles/Weekly_beetle_community_richness/collection.json
deleted file mode 100644
index add58e1127..0000000000
--- a/catalog/scores/Beetles/Weekly_beetle_community_richness/collection.json
+++ /dev/null
@@ -1,202 +0,0 @@
-{
-  "id": "Weekly_beetle_community_richness",
-  "description": "This page includes all models for the Weekly_beetle_community_richness variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Weekly_beetle_community_richness",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-02T00:00:00Z",
-          "2023-10-16T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "observation",
-      "type": "double",
-      "description": "observed value for variable"
-    },
-    {
-      "name": "crps",
-      "type": "double",
-      "description": "crps forecast score"
-    },
-    {
-      "name": "logs",
-      "type": "double",
-      "description": "logs forecast score"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique project identifier"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "date",
-      "type": "string",
-      "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=P1W/variable=richness?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=P1W/variable=richness?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/scores/Beetles/collection.json b/catalog/scores/Beetles/collection.json
deleted file mode 100644
index 2bd9aa00e3..0000000000
--- a/catalog/scores/Beetles/collection.json
+++ /dev/null
@@ -1,192 +0,0 @@
-{
-  "id": "Beetles",
-  "description": "This page includes variables for the Beetles group.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "Weekly_beetle_community_abundance/collection.json"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "Weekly_beetle_community_richness/collection.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Beetles",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-01T00:00:00Z",
-          "2023-12-15T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "observation",
-      "type": "double",
-      "description": "observed value for variable"
-    },
-    {
-      "name": "crps",
-      "type": "double",
-      "description": "crps forecast score"
-    },
-    {
-      "name": "logs",
-      "type": "double",
-      "description": "logs forecast score"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique project identifier"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "date",
-      "type": "string",
-      "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for the NEON Ecological Forecasting Aquatics theme.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |>\n dplyr::filter(variable %in% c(\"abundance\", \"richness\")) |>\n dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "https://www.neonscience.org/sites/default/files/styles/max_width_1170px/public/image-content-images/Beetles_pinned.jpg",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "Beetle Image"
-    }
-  }
-}
diff --git a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/collection.json b/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/collection.json
deleted file mode 100644
index 29855e4592..0000000000
--- a/catalog/scores/Phenology/Daily_Green_chromatic_coordinate/collection.json
+++ /dev/null
@@ -1,267 +0,0 @@
-{
-  "id": "Daily_Green_chromatic_coordinate",
-  "description": "This page includes all models for the Daily_Green_chromatic_coordinate variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_prophet.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/climatology.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/persistenceRW.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_bag_mlp.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Daily_Green_chromatic_coordinate",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-01T00:00:00Z",
-          "2023-12-15T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "observation",
-      "type": "double",
-      "description": "observed value for variable"
-    },
-    {
-      "name": "crps",
-      "type": "double",
-      "description": "crps forecast score"
-    },
-    {
-      "name": "logs",
-      "type": "double",
-      "description": "logs forecast score"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique project identifier"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "date",
-      "type": "string",
-      "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=P1D/variable=gcc_90?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=P1D/variable=gcc_90?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/collection.json b/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/collection.json
deleted file mode 100644
index ab49c47126..0000000000
--- a/catalog/scores/Phenology/Daily_Red_chromatic_coordinate/collection.json
+++ /dev/null
@@ -1,282 +0,0 @@
-{
-  "id": "Daily_Red_chromatic_coordinate",
-  "description": "This page includes all models for the Daily_Red_chromatic_coordinate variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/baseline_ensemble.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_prophet.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/climatology.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/persistenceRW.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/prophet_clim_ensemble.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_bag_mlp.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Daily_Red_chromatic_coordinate",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-01T00:00:00Z",
-          "2023-12-15T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "observation",
-      "type": "double",
-      "description": "observed value for variable"
-    },
-    {
-      "name": "crps",
-      "type": "double",
-      "description": "crps forecast score"
-    },
-    {
-      "name": "logs",
-      "type": "double",
-      "description": "logs forecast score"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique project identifier"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "date",
-      "type": "string",
-      "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=P1D/variable=rcc_90?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=P1D/variable=rcc_90?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/scores/Phenology/collection.json b/catalog/scores/Phenology/collection.json
deleted file mode 100644
index 32c41b83c8..0000000000
--- a/catalog/scores/Phenology/collection.json
+++ /dev/null
@@ -1,192 +0,0 @@
-{
-  "id": "Phenology",
-  "description": "This page includes variables for the Phenology group.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "Daily_Green_chromatic_coordinate/collection.json"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "Daily_Red_chromatic_coordinate/collection.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Phenology",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-01T00:00:00Z",
-          "2023-12-15T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "observation",
-      "type": "double",
-      "description": "observed value for variable"
-    },
-    {
-      "name": "crps",
-      "type": "double",
-      "description": "crps forecast score"
-    },
-    {
-      "name": "logs",
-      "type": "double",
-      "description": "logs forecast score"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique project identifier"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "date",
-      "type": "string",
-      "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for the NEON Ecological Forecasting Aquatics theme.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |>\n dplyr::filter(variable %in% c(\"gcc_90\", \"rcc_90\")) |>\n dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "https://www.neonscience.org/sites/default/files/styles/max_1300x1300/public/image-content-images/_BFP8455.jpg",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "Phenology Image"
-    }
-  }
-}
diff --git a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/collection.json b/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/collection.json
deleted file mode 100644
index d9cc1ca059..0000000000
--- a/catalog/scores/Terrestrial/Daily_Net_ecosystem_exchange/collection.json
+++ /dev/null
@@ -1,262 +0,0 @@
-{
-  "id": "Daily_Net_ecosystem_exchange",
-  "description": "This page includes all models for the Daily_Net_ecosystem_exchange variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_prophet.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/climatology.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_bag_mlp.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Daily_Net_ecosystem_exchange",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-01T00:00:00Z",
-          "2023-12-09T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "observation",
-      "type": "double",
-      "description": "observed value for variable"
-    },
-    {
-      "name": "crps",
-      "type": "double",
-      "description": "crps forecast score"
-    },
-    {
-      "name": "logs",
-      "type": "double",
-      "description": "logs forecast score"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique project identifier"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "date",
-      "type": "string",
-      "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=P1D/variable=le?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=P1D/variable=le?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/scores/Terrestrial/collection.json b/catalog/scores/Terrestrial/collection.json
deleted file mode 100644
index b2f5808086..0000000000
--- a/catalog/scores/Terrestrial/collection.json
+++ /dev/null
@@ -1,202 +0,0 @@
-{
-  "id": "Terrestrial",
-  "description": "This page includes variables for the Terrestrial group.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "Daily_Net_ecosystem_exchange/collection.json"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "Daily_Net_ecosystem_exchange/collection.json"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "30min_latent_heat_flux/collection.json"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "30min_latent_heat_flux/collection.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Terrestrial",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-01T00:00:00Z",
-          "2023-12-15T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "observation",
-      "type": "double",
-      "description": "observed value for variable"
-    },
-    {
-      "name": "crps",
-      "type": "double",
-      "description": "crps forecast score"
-    },
-    {
-      "name": "logs",
-      "type": "double",
-      "description": "logs forecast score"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique project identifier"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "date",
-      "type": "string",
-      "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for the NEON Ecological Forecasting Aquatics theme.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |>\n dplyr::filter(variable %in% c(\"nee\", \"le\")) |>\n dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "https://projects.ecoforecast.org/neon4cast-catalog/img/BONA_Twr.jpg",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "NEON Field Tower"
-    }
-  }
-}
diff --git a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/collection.json b/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/collection.json
deleted file mode 100644
index 69d97212a6..0000000000
--- a/catalog/scores/Ticks/Weekly_Amblyomma_americanum_population/collection.json
+++ /dev/null
@@ -1,202 +0,0 @@
-{
-  "id": "Weekly_Amblyomma_americanum_population",
-  "description": "This page includes all models for the Weekly_Amblyomma_americanum_population variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Weekly_Amblyomma_americanum_population",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-96.5631, 29.6893, -76.56, 39.1008]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-02T00:00:00Z",
-          "2023-07-03T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "observation",
-      "type": "double",
-      "description": "observed value for variable"
-    },
-    {
-      "name": "crps",
-      "type": "double",
-      "description": "crps forecast score"
-    },
-    {
-      "name": "logs",
-      "type": "double",
-      "description": "logs forecast score"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique project identifier"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "date",
-      "type": "string",
-      "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/scores/Terrestrial/30min_latent_heat_flux/collection.json b/catalog/scores/aquatics/Daily_Chlorophyll_a/collection.json
similarity index 80%
rename from catalog/scores/Terrestrial/30min_latent_heat_flux/collection.json
rename to catalog/scores/aquatics/Daily_Chlorophyll_a/collection.json
index c93dc3f4d5..21dd28a1d7 100644
--- a/catalog/scores/Terrestrial/30min_latent_heat_flux/collection.json
+++ b/catalog/scores/aquatics/Daily_Chlorophyll_a/collection.json
@@ -1,6 +1,6 @@
 {
-  "id": "30min_latent_heat_flux",
-  "description": "This page includes all models for the 30min_latent_heat_flux variable.",
+  "id": "Daily_Chlorophyll_a",
+  "description": "This page includes all models for the Daily_Chlorophyll_a variable.",
   "stac_version": "1.0.0",
   "license": "CC0-1.0",
   "stac_extensions": [
@@ -10,6 +10,11 @@
   ],
   "type": "Collection",
   "links": [
+    {
+      "rel": "item",
+      "type": "application/json",
+      "href": "../../models/model_items/persistenceRW.json"
+    },
     {
       "rel": "item",
       "type": "application/json",
@@ -36,29 +41,29 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
-  "title": "30min_latent_heat_flux",
+  "title": "Daily_Chlorophyll_a",
   "extent": {
     "spatial": {
       "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
+        ["Inf", "Inf", "-Inf", "-Inf"]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2023-11-14T00:00:00Z",
-          "2023-12-09T00:00:00Z"
+          "2024-02-07T00:00:00Z",
+          "2024-02-09T00:00:00Z"
         ]
       ]
     }
@@ -167,13 +172,13 @@
   ],
   "assets": {
     "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=PT30M/variable=le?endpoint_override=sdsc.osn.xsede.org\"",
+      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=usgsrc4cast/duration=P1D/variable=chla?endpoint_override=sdsc.osn.xsede.org",
       "type": "application/x-parquet",
       "title": "Database Access",
       "roles": [
         "data"
       ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=neon4cast/duration=PT30M/variable=le?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/project_id=usgsrc4cast/duration=P1D/variable=chla?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     },
     "thumbnail": {
       "href": "pending",
diff --git a/catalog/scores/Ticks/collection.json b/catalog/scores/aquatics/collection.json
similarity index 81%
rename from catalog/scores/Ticks/collection.json
rename to catalog/scores/aquatics/collection.json
index 6420aa4511..3b37c864e9 100644
--- a/catalog/scores/Ticks/collection.json
+++ b/catalog/scores/aquatics/collection.json
@@ -1,6 +1,6 @@
 {
-  "id": "Ticks",
-  "description": "This page includes variables for the Ticks group.",
+  "id": "aquatics",
+  "description": "This page includes variables for the aquatics group.",
   "stac_version": "1.0.0",
   "license": "CC0-1.0",
   "stac_extensions": [
@@ -13,7 +13,7 @@
     {
       "rel": "child",
       "type": "application/json",
-      "href": "Weekly_Amblyomma_americanum_population/collection.json"
+      "href": "Daily_Chlorophyll_a/collection.json"
     },
     {
       "rel": "parent",
@@ -36,29 +36,29 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
-  "title": "Ticks",
+  "title": "aquatics",
   "extent": {
     "spatial": {
       "bbox": [
-        [-96.5631, 29.6893, -76.56, 39.1008]
+        ["Inf", "Inf", "-Inf", "-Inf"]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2023-01-01T00:00:00Z",
-          "2023-12-15T00:00:00Z"
+          "2024-02-07T00:00:00Z",
+          "2024-02-09T00:00:00Z"
         ]
       ]
     }
@@ -167,21 +167,21 @@
   ],
   "assets": {
     "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/?endpoint_override=sdsc.osn.xsede.org\"",
+      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/?endpoint_override=sdsc.osn.xsede.org",
       "type": "application/x-parquet",
       "title": "Database Access",
       "roles": [
         "data"
       ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for the NEON Ecological Forecasting Aquatics theme.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |>\n dplyr::filter(variable %in% c(\"amblyomma_americanum\")) |>\n dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `arrow` for remote access to the database. This R code will return results for the NEON Ecological Forecasting Aquatics theme.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |>\n dplyr::filter(variable %in% c(\"chla\")) |>\n dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     },
     "thumbnail": {
-      "href": "https://raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/thumbnail_plots/tick_drag.jpg",
+      "href": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/thumbnails/image/Back-b.jpg",
       "type": "image/JPEG",
       "roles": [
         "thumbnail"
       ],
-      "title": "NEON Ticks"
+      "title": "USGS Streamgage"
     }
   }
 }
diff --git a/catalog/scores/collection.json b/catalog/scores/collection.json
index a8a812ea55..ca551f7e4b 100644
--- a/catalog/scores/collection.json
+++ b/catalog/scores/collection.json
@@ -1,6 +1,6 @@
 {
   "id": "daily-scores",
-  "description": "The catalog contains scores for the NEON Ecological Forecasting Challenge. The scores are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations). You can access the scores at the top level of the dataset where all models, variables, and dates that forecasts were produced (reference_datetime) are available. The code to access the entire dataset is provided as an asset. Given the size of the scores catalog, it can be time-consuming to access the data at the full dataset level. For quicker access to the scores for a particular model (model_id), we also provide the code to access the data at the model_id level as an asset for each model.",
+  "description": "The catalog contains scores for the EFI-USGS River Chlorophyll Forecasting Challenge. The scores are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations). You can access the scores at the top level of the dataset where all models, variables, and dates that forecasts were produced (reference_datetime) are available. The code to access the entire dataset is provided as an asset. Given the size of the scores catalog, it can be time-consuming to access the data at the full dataset level. For quicker access to the scores for a particular model (model_id), we also provide the code to access the data at the model_id level as an asset for each model.",
   "stac_version": "1.0.0",
   "license": "CC0-1.0",
   "stac_extensions": [
@@ -13,32 +13,8 @@
     {
       "rel": "child",
       "type": "application/json",
-      "href": "Aquatics/collection.json",
-      "title": "Aquatics"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "Terrestrial/collection.json",
-      "title": "Terrestrial"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "Phenology/collection.json",
-      "title": "Phenology"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "Beetles/collection.json",
-      "title": "Beetles"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "Ticks/collection.json",
-      "title": "Ticks"
+      "href": "aquatics/collection.json",
+      "title": "aquatics"
     },
     {
       "rel": "child",
@@ -67,14 +43,14 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
@@ -83,18 +59,18 @@
     "spatial": {
       "bbox": [
         [
-          -156.6194,
-          17.9696,
-          -66.7987,
-          71.2824
+          -122.6692,
+          39.6327,
+          -74.7781,
+          45.5175
         ]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2023-01-01T00:00:00Z",
-          "2023-12-15T00:00:00Z"
+          "2024-02-07T00:00:00Z",
+          "2024-02-09T00:00:00Z"
         ]
       ]
     }
@@ -203,21 +179,21 @@
   ],
   "assets": {
     "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/?endpoint_override=sdsc.osn.xsede.org\"",
+      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/?endpoint_override=sdsc.osn.xsede.org",
       "type": "application/x-parquet",
       "title": "Database Access",
       "roles": [
         "data"
       ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for the VERA Forecasting Challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `arrow` for remote access to the database. This R code will return results for the Forecasting Challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     },
     "thumbnail": {
-      "href": "https://raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/thumbnail_plots/52760199990_d1a0f154fe_c.jpg",
+      "href": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/thumbnails/image/Back-b.jpg",
       "type": "image/JPEG",
       "roles": [
         "thumbnail"
       ],
-      "title": "NEON Buoy Image"
+      "title": "USGS Streamgage"
     }
   }
 }
diff --git a/catalog/scores/models/collection.json b/catalog/scores/models/collection.json
index bf6aeade84..7cda4dea03 100644
--- a/catalog/scores/models/collection.json
+++ b/catalog/scores/models/collection.json
@@ -1,6 +1,6 @@
 {
   "id": "models",
-  "description": "The catalog contains scores for the NEON Ecological Forecasting Challenge. The scores are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations). You can access the scores at the top level of the dataset where all models, variables, and dates that forecasts were produced (reference_datetime) are available. The code to access the entire dataset is provided as an asset. Given the size of the scores catalog, it can be time-consuming to access the data at the full dataset level. For quicker access to the scores for a particular model (model_id), we also provide the code to access the data at the model_id level as an asset for each model.",
+  "description": "The catalog contains scores for the EFI-USGS River Chlorophyll Forecasting Challenge. The scores are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations). You can access the scores at the top level of the dataset where all models, variables, and dates that forecasts were produced (reference_datetime) are available. The code to access the entire dataset is provided as an asset. Given the size of the scores catalog, it can be time-consuming to access the data at the full dataset level. For quicker access to the scores for a particular model (model_id), we also provide the code to access the data at the model_id level as an asset for each model.",
   "stac_version": "1.0.0",
   "license": "CC0-1.0",
   "stac_extensions": [
@@ -10,21 +10,6 @@
   ],
   "type": "Collection",
   "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/USGSHABs1.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/cb_prophet.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/climatology.json"
-    },
     {
       "rel": "item",
       "type": "application/json",
@@ -33,222 +18,7 @@
     {
       "rel": "item",
       "type": "application/json",
-      "href": "model_items/procBlanchardMonod.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procBlanchardSteele.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procCTMIMonod.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procCTMISteele.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procEppleyNorbergMonod.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procEppleyNorbergSteele.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procHinshelwoodMonod.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procHinshelwoodSteele.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_bag_mlp.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_lasso_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/GLEON_lm_lag_1day.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/air2waterSat_2.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/cb_f1.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/null.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_randfor_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/baseline_ensemble.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/prophet_clim_ensemble.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/fARIMA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/fARIMA_clim_ensemble.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/fTSLM_lag.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/GLEON_JRabaey_temp_physics.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/GLEON_physics.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flareGLM.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flareGLM_noDA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flareGOTM.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flareGOTM_noDA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flareSimstrat.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flareSimstrat_noDA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flare_ler.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flare_ler_baselines.json"
+      "href": "model_items/climatology.json"
     },
     {
       "rel": "parent",
@@ -271,14 +41,14 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
@@ -286,14 +56,14 @@
   "extent": {
     "spatial": {
       "bbox": [
-        [-156.6194, 17.9696, -66.7987, 71.2824]
+        [-122.6692, 39.6328, -74.7781, 45.5175]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2023-01-01T00:00:00Z",
-          "2023-12-15T00:00:00Z"
+          "2024-02-07T00:00:00Z",
+          "2024-02-09T00:00:00Z"
         ]
       ]
     }
@@ -402,7 +172,7 @@
   ],
   "assets": {
     "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/scores/parquet/?endpoint_override=sdsc.osn.xsede.org\"",
+      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/?endpoint_override=sdsc.osn.xsede.org",
       "type": "application/x-parquet",
       "title": "Database Access",
       "roles": [
diff --git a/catalog/scores/models/model_items/.empty b/catalog/scores/models/model_items/.empty
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/catalog/scores/models/model_items/GLEON_JRabaey_temp_physics.json b/catalog/scores/models/model_items/GLEON_JRabaey_temp_physics.json
deleted file mode 100644
index 5ab7f516e8..0000000000
--- a/catalog/scores/models/model_items/GLEON_JRabaey_temp_physics.json
+++ /dev/null
@@ -1,239 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "GLEON_JRabaey_temp_physics",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "GLEON_JRabaey_temp_physics"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "GLEON_JRabaey_temp_physics"
-    },
-    {
-      "rel": "self",
-      "href": "GLEON_JRabaey_temp_physics.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_JRabaey_temp_physics.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_JRabaey_temp_physics.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=GLEON_JRabaey_temp_physics?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=GLEON_JRabaey_temp_physics?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/GLEON_lm_lag_1day.json b/catalog/scores/models/model_items/GLEON_lm_lag_1day.json
deleted file mode 100644
index 875dba6da6..0000000000
--- a/catalog/scores/models/model_items/GLEON_lm_lag_1day.json
+++ /dev/null
@@ -1,219 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "GLEON_lm_lag_1day",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Dissolved_oxygen, Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "GLEON_lm_lag_1day"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "GLEON_lm_lag_1day"
-    },
-    {
-      "rel": "self",
-      "href": "GLEON_lm_lag_1day.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_lm_lag_1day.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_lm_lag_1day.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/GLEON_physics.json b/catalog/scores/models/model_items/GLEON_physics.json
deleted file mode 100644
index 7789ea4e4d..0000000000
--- a/catalog/scores/models/model_items/GLEON_physics.json
+++ /dev/null
@@ -1,211 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "GLEON_physics",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "GLEON_physics"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "GLEON_physics"
-    },
-    {
-      "rel": "self",
-      "href": "GLEON_physics.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_physics.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_physics.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=GLEON_physics?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=GLEON_physics?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/TESTclimatology.json b/catalog/scores/models/model_items/TESTclimatology.json
deleted file mode 100644
index e05cea5e88..0000000000
--- a/catalog/scores/models/model_items/TESTclimatology.json
+++ /dev/null
@@ -1,200 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "TESTclimatology",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [37.3129, -79.8159],
-      [37.3032, -79.8372]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-09-22",
-    "end_datetime": "2023-10-27",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "vera4cast",
-      "Temp_C_mean"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "depth_m",
-        "type": "double",
-        "description": "depth (meters) in water column of prediction"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "TESTclimatology"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "TESTclimatology"
-    },
-    {
-      "rel": "self",
-      "href": "TESTclimatology.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/TESTclimatology.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/TESTclimatology.json\")\n\n"
-    },
-    "2": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Temp_C_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=TESTclimatology?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=TESTclimatology?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/USGSHABs1.json b/catalog/scores/models/model_items/USGSHABs1.json
deleted file mode 100644
index 23de7335e0..0000000000
--- a/catalog/scores/models/model_items/USGSHABs1.json
+++ /dev/null
@@ -1,208 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "USGSHABs1",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-87.7982, 32.5415],
-      [-84.4374, 31.1854],
-      [-88.1589, 31.8534]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BLWA, FLNT, TOMB\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-12",
-    "end_datetime": "2023-11-30",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "USGSHABs1"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "USGSHABs1"
-    },
-    {
-      "rel": "self",
-      "href": "USGSHABs1.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/USGSHABs1.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/USGSHABs1.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=USGSHABs1?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=USGSHABs1?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/air2waterSat_2.json b/catalog/scores/models/model_items/air2waterSat_2.json
deleted file mode 100644
index 8a5ec9fbdd..0000000000
--- a/catalog/scores/models/model_items/air2waterSat_2.json
+++ /dev/null
@@ -1,246 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "air2waterSat_2",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU\n\nVariables: Daily Dissolved_oxygen, Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "air2waterSat_2"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "air2waterSat_2"
-    },
-    {
-      "rel": "self",
-      "href": "air2waterSat_2.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/air2waterSat_2.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/air2waterSat_2.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/baseline_ensemble.json b/catalog/scores/models/model_items/baseline_ensemble.json
deleted file mode 100644
index 4866fbf032..0000000000
--- a/catalog/scores/models/model_items/baseline_ensemble.json
+++ /dev/null
@@ -1,281 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "baseline_ensemble",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-122.3303, 45.7624],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
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-      [-72.1727, 42.5369],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
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-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
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-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
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-      [-119.006, 37.0058],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
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-      [-121.9519, 45.8205],
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-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU\n\nVariables: Daily Red_chromatic_coordinate, Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "baseline_ensemble"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "baseline_ensemble"
-    },
-    {
-      "rel": "self",
-      "href": "baseline_ensemble.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/baseline_ensemble.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/baseline_ensemble.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/cb_f1.json b/catalog/scores/models/model_items/cb_f1.json
deleted file mode 100644
index b433c61875..0000000000
--- a/catalog/scores/models/model_items/cb_f1.json
+++ /dev/null
@@ -1,246 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "cb_f1",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-10-11",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "cb_f1"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "cb_f1"
-    },
-    {
-      "rel": "self",
-      "href": "cb_f1.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/cb_f1.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/cb_f1.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=cb_f1?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=cb_f1?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=cb_f1?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=cb_f1?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/cb_prophet.json b/catalog/scores/models/model_items/cb_prophet.json
deleted file mode 100644
index 87e55271a8..0000000000
--- a/catalog/scores/models/model_items/cb_prophet.json
+++ /dev/null
@@ -1,326 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "cb_prophet",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "cb_prophet"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "cb_prophet"
-    },
-    {
-      "rel": "self",
-      "href": "cb_prophet.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/cb_prophet.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/cb_prophet.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/cfs.json b/catalog/scores/models/model_items/cfs.json
deleted file mode 100644
index 96f7d7a0ca..0000000000
--- a/catalog/scores/models/model_items/cfs.json
+++ /dev/null
@@ -1,223 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "cfs",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [37.3032, -79.8372]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NOAA Climate Forecasting System forecasts as downloaded from open-meteo.com.  Submitted forecasts only include the first ~6 months because there are 4 ensemble members available (only 1 is available from 6 - 9 months).\n\nSites: fcre\n\nVariables: AirTemp_C_mean, RH_percent_mean, Rain_mm_sum, WindSpeed_ms_mean, ShortwaveRadiationUp_Wm2_mean",
-    "start_datetime": "2023-10-13",
-    "end_datetime": "2024-02-01",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "vera4cast",
-      "AirTemp_C_mean, RH_percent_mean, Rain_mm_sum, WindSpeed_ms_mean, ShortwaveRadiationUp_Wm2_mean"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "depth_m",
-        "type": "double",
-        "description": "depth (meters) in water column of prediction"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "cfs"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "cfs"
-    },
-    {
-      "rel": "self",
-      "href": "cfs.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/cfs.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/cfs.json\")\n\n"
-    },
-    "2": {
-      "type": "application/x-parquet",
-      "title": "Database Access for AirTemp_C_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for RH_percent_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Rain_mm_sum daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=cfs?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=cfs?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for WindSpeed_ms_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for ShortwaveRadiationUp_Wm2_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=cfs?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/climatology.json b/catalog/scores/models/model_items/climatology.json
index 490b1f7c08..df69da2ac9 100644
--- a/catalog/scores/models/model_items/climatology.json
+++ b/catalog/scores/models/model_items/climatology.json
@@ -7,95 +7,30 @@
   "id": "climatology",
   "bbox": [
     [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
+      -122.6692,
+      45.5175,
+      -74.7781,
+      45.5175
     ]
   ],
   "geometry": {
     "type": "MultiPoint",
     "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-84.4374, 31.1854],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-122.3303, 45.7624],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-156.6194, 71.2824],
-      [-147.5026, 65.154],
-      [-145.7514, 63.8811],
-      [-149.2133, 63.8758],
-      [-149.3705, 68.6611],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      []
     ]
   },
   "properties": {
-    "description": ["\nmodel info: Historical DOY mean and sd.  Assumes normal distribution\n\n\nSites: BARC, BLWA, FLNT, SUGG, TOMB, ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BARR, BONA, DEJU, HEAL, TOOL, ARIK, BIGC, BLDE, BLUE, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, 30min latent_heat_flux, Daily Net_ecosystem_exchange, 30min Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature", "\nmodel info: NA\n\nSites: BARC, BLWA, FLNT, SUGG, TOMB, ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BARR, BONA, DEJU, HEAL, TOOL, ARIK, BIGC, BLDE, BLUE, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, 30min latent_heat_flux, Daily Net_ecosystem_exchange, 30min Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature"],
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
+    "description": "\nmodel info: Forecasts stream chlorophyll-a based on the historic average and standard deviation for that given site and day-of-year.\n\nSites: USGS-01427510, USGS-01463500, USGS-05543010, USGS-05553700, USGS-05558300, USGS-05586300, USGS-14181500, USGS-14211010, USGS-14211720\n\nVariables: Daily Chlorophyll_a",
+    "start_datetime": "2024-02-07",
+    "end_datetime": "2024-02-09",
     "providers": [
       {
         "url": "pending",
@@ -117,16 +52,8 @@
     "license": "CC0-1.0",
     "keywords": [
       "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily latent_heat_flux",
-      "30min latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "30min Net_ecosystem_exchange",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature"
+      "usgsrc4cast",
+      "Daily Chlorophyll_a"
     ],
     "table:columns": [
       {
@@ -280,56 +207,8 @@
     "3": {
       "type": "application/x-parquet",
       "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for 30min latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=PT30M/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=PT30M/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for 30min Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=PT30M/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=PT30M/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "href": "s3://anonymous@bio230014-bucket01/challenges/scoresproject_id=/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
+      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scoresproject_id=/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     }
   }
 }
diff --git a/catalog/scores/models/model_items/climatology2.json b/catalog/scores/models/model_items/climatology2.json
deleted file mode 100644
index e2cd083bee..0000000000
--- a/catalog/scores/models/model_items/climatology2.json
+++ /dev/null
@@ -1,206 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "climatology2",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [37.3129, -79.8159],
-      [37.3032, -79.8372]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: Same is the other climatology\n\nSites: bvre, fcre\n\nVariables: Chla_ugL_mean, Temp_C_mean",
-    "start_datetime": "2023-10-07",
-    "end_datetime": "2023-11-10",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "vera4cast",
-      "Chla_ugL_mean, Temp_C_mean"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "depth_m",
-        "type": "double",
-        "description": "depth (meters) in water column of prediction"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "climatology2"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "climatology2"
-    },
-    {
-      "rel": "self",
-      "href": "climatology2.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/climatology2.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/climatology2.json\")\n\n"
-    },
-    "2": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Chla_ugL_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=climatology2?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=climatology2?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Temp_C_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=climatology2?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=climatology2?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/ecmwf_ifs04.json b/catalog/scores/models/model_items/ecmwf_ifs04.json
deleted file mode 100644
index 0416919ca0..0000000000
--- a/catalog/scores/models/model_items/ecmwf_ifs04.json
+++ /dev/null
@@ -1,223 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "ecmwf_ifs04",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [37.3032, -79.8372]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: ECMWF IFS Ensemble weather model downloaded from open-meteo.com.  Since ECMWF IFS Ensemble does output solar radiation on open-meteo.com,  the solar radiation from GFS seamless is used.\n\nSites: fcre\n\nVariables: AirTemp_C_mean, RH_percent_mean, Rain_mm_sum, BP_kPa_mean, WindSpeed_ms_mean",
-    "start_datetime": "2023-10-14",
-    "end_datetime": "2023-11-08",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "vera4cast",
-      "AirTemp_C_mean, RH_percent_mean, Rain_mm_sum, BP_kPa_mean, WindSpeed_ms_mean"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "depth_m",
-        "type": "double",
-        "description": "depth (meters) in water column of prediction"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "ecmwf_ifs04"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "ecmwf_ifs04"
-    },
-    {
-      "rel": "self",
-      "href": "ecmwf_ifs04.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/ecmwf_ifs04.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/ecmwf_ifs04.json\")\n\n"
-    },
-    "2": {
-      "type": "application/x-parquet",
-      "title": "Database Access for AirTemp_C_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for RH_percent_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Rain_mm_sum daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for BP_kPa_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=BP_kPa_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=BP_kPa_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for WindSpeed_ms_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=ecmwf_ifs04?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/fARIMA.json b/catalog/scores/models/model_items/fARIMA.json
deleted file mode 100644
index c3996fc398..0000000000
--- a/catalog/scores/models/model_items/fARIMA.json
+++ /dev/null
@@ -1,239 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "fARIMA",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-10",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fARIMA"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fARIMA"
-    },
-    {
-      "rel": "self",
-      "href": "fARIMA.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fARIMA.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fARIMA.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=fARIMA?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=fARIMA?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/fARIMA_clim_ensemble.json b/catalog/scores/models/model_items/fARIMA_clim_ensemble.json
deleted file mode 100644
index 6afb2c8b29..0000000000
--- a/catalog/scores/models/model_items/fARIMA_clim_ensemble.json
+++ /dev/null
@@ -1,233 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "fARIMA_clim_ensemble",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-122.1655, 44.2596],
-      [-88.1589, 31.8534],
-      [-119.2575, 37.0597],
-      [-119.0274, 36.9559]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: ARIK, BARC, BLDE, BLUE, BLWA, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, POSE, PRIN, REDB, SUGG, SYCA, WALK, WLOU, MCRA, TOMB, BIGC, TECR\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-10",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fARIMA_clim_ensemble"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fARIMA_clim_ensemble"
-    },
-    {
-      "rel": "self",
-      "href": "fARIMA_clim_ensemble.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fARIMA_clim_ensemble.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fARIMA_clim_ensemble.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=fARIMA_clim_ensemble?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=fARIMA_clim_ensemble?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/fTSLM_lag.json b/catalog/scores/models/model_items/fTSLM_lag.json
deleted file mode 100644
index 134cf7038b..0000000000
--- a/catalog/scores/models/model_items/fTSLM_lag.json
+++ /dev/null
@@ -1,239 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "fTSLM_lag",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-10",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fTSLM_lag"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fTSLM_lag"
-    },
-    {
-      "rel": "self",
-      "href": "fTSLM_lag.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fTSLM_lag.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fTSLM_lag.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=fTSLM_lag?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=fTSLM_lag?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/fableARIMA.json b/catalog/scores/models/model_items/fableARIMA.json
deleted file mode 100644
index 66c6cd32d1..0000000000
--- a/catalog/scores/models/model_items/fableARIMA.json
+++ /dev/null
@@ -1,206 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "fableARIMA",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [37.3032, -79.8372],
-      [37.3129, -79.8159]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: ARIMA fit using the ARIMA() function in the fable R package\n\nSites: fcre, bvre\n\nVariables: Chla_ugL_mean, Bloom_binary_mean",
-    "start_datetime": "2023-10-12",
-    "end_datetime": "2023-12-04",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "vera4cast",
-      "Chla_ugL_mean, Bloom_binary_mean"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "depth_m",
-        "type": "double",
-        "description": "depth (meters) in water column of prediction"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fableARIMA"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fableARIMA"
-    },
-    {
-      "rel": "self",
-      "href": "fableARIMA.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableARIMA.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableARIMA.json\")\n\n"
-    },
-    "2": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Chla_ugL_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Bloom_binary_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Bloom_binary_mean/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Bloom_binary_mean/model_id=fableARIMA?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/fableETS.json b/catalog/scores/models/model_items/fableETS.json
deleted file mode 100644
index 80fd415961..0000000000
--- a/catalog/scores/models/model_items/fableETS.json
+++ /dev/null
@@ -1,206 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "fableETS",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [37.3129, -79.8159],
-      [37.3032, -79.8372]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: fable package exponential smoothing model fable::ETS()\n\nSites: bvre, fcre\n\nVariables: Chla_ugL_mean, Bloom_binary_mean",
-    "start_datetime": "2023-10-12",
-    "end_datetime": "2023-12-04",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "vera4cast",
-      "Chla_ugL_mean, Bloom_binary_mean"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "depth_m",
-        "type": "double",
-        "description": "depth (meters) in water column of prediction"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fableETS"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fableETS"
-    },
-    {
-      "rel": "self",
-      "href": "fableETS.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableETS.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableETS.json\")\n\n"
-    },
-    "2": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Chla_ugL_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=fableETS?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=fableETS?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Bloom_binary_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Bloom_binary_mean/model_id=fableETS?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Bloom_binary_mean/model_id=fableETS?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/fableNNETAR.json b/catalog/scores/models/model_items/fableNNETAR.json
deleted file mode 100644
index a419febdf6..0000000000
--- a/catalog/scores/models/model_items/fableNNETAR.json
+++ /dev/null
@@ -1,206 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "fableNNETAR",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [37.3129, -79.8159],
-      [37.3032, -79.8372]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: autoregressive neural net fit using the NNETAR() function in the fable R package\n\nSites: bvre, fcre\n\nVariables: Bloom_binary_mean, Chla_ugL_mean",
-    "start_datetime": "2023-10-12",
-    "end_datetime": "2023-12-04",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "vera4cast",
-      "Bloom_binary_mean, Chla_ugL_mean"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "depth_m",
-        "type": "double",
-        "description": "depth (meters) in water column of prediction"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fableNNETAR"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fableNNETAR"
-    },
-    {
-      "rel": "self",
-      "href": "fableNNETAR.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableNNETAR.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/fableNNETAR.json\")\n\n"
-    },
-    "2": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Bloom_binary_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Bloom_binary_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Bloom_binary_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Chla_ugL_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=fableNNETAR?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/flareGLM.json b/catalog/scores/models/model_items/flareGLM.json
deleted file mode 100644
index 1bd2d79a12..0000000000
--- a/catalog/scores/models/model_items/flareGLM.json
+++ /dev/null
@@ -1,212 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flareGLM",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGLM"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGLM"
-    },
-    {
-      "rel": "self",
-      "href": "flareGLM.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGLM.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGLM.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareGLM?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareGLM?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/flareGLM_noDA.json b/catalog/scores/models/model_items/flareGLM_noDA.json
deleted file mode 100644
index 5e785d737e..0000000000
--- a/catalog/scores/models/model_items/flareGLM_noDA.json
+++ /dev/null
@@ -1,212 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flareGLM_noDA",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGLM_noDA"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGLM_noDA"
-    },
-    {
-      "rel": "self",
-      "href": "flareGLM_noDA.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGLM_noDA.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGLM_noDA.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareGLM_noDA?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareGLM_noDA?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/flareGOTM.json b/catalog/scores/models/model_items/flareGOTM.json
deleted file mode 100644
index c7c18a1b4b..0000000000
--- a/catalog/scores/models/model_items/flareGOTM.json
+++ /dev/null
@@ -1,211 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flareGOTM",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGOTM"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGOTM"
-    },
-    {
-      "rel": "self",
-      "href": "flareGOTM.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGOTM.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGOTM.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareGOTM?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareGOTM?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/flareGOTM_noDA.json b/catalog/scores/models/model_items/flareGOTM_noDA.json
deleted file mode 100644
index d991c1fb86..0000000000
--- a/catalog/scores/models/model_items/flareGOTM_noDA.json
+++ /dev/null
@@ -1,211 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flareGOTM_noDA",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGOTM_noDA"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGOTM_noDA"
-    },
-    {
-      "rel": "self",
-      "href": "flareGOTM_noDA.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGOTM_noDA.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGOTM_noDA.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareGOTM_noDA?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareGOTM_noDA?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/flareSimstrat.json b/catalog/scores/models/model_items/flareSimstrat.json
deleted file mode 100644
index f742d38533..0000000000
--- a/catalog/scores/models/model_items/flareSimstrat.json
+++ /dev/null
@@ -1,211 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flareSimstrat",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareSimstrat"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareSimstrat"
-    },
-    {
-      "rel": "self",
-      "href": "flareSimstrat.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareSimstrat.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareSimstrat.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareSimstrat?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareSimstrat?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/flareSimstrat_noDA.json b/catalog/scores/models/model_items/flareSimstrat_noDA.json
deleted file mode 100644
index 90e3694f2d..0000000000
--- a/catalog/scores/models/model_items/flareSimstrat_noDA.json
+++ /dev/null
@@ -1,210 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flareSimstrat_noDA",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, PRLA, PRPO, SUGG\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareSimstrat_noDA"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareSimstrat_noDA"
-    },
-    {
-      "rel": "self",
-      "href": "flareSimstrat_noDA.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareSimstrat_noDA.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareSimstrat_noDA.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareSimstrat_noDA?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flareSimstrat_noDA?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/flare_ler.json b/catalog/scores/models/model_items/flare_ler.json
deleted file mode 100644
index c0c09632a0..0000000000
--- a/catalog/scores/models/model_items/flare_ler.json
+++ /dev/null
@@ -1,211 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flare_ler",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flare_ler"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flare_ler"
-    },
-    {
-      "rel": "self",
-      "href": "flare_ler.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flare_ler.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flare_ler.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flare_ler?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flare_ler?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/flare_ler_baselines.json b/catalog/scores/models/model_items/flare_ler_baselines.json
deleted file mode 100644
index e7098681c6..0000000000
--- a/catalog/scores/models/model_items/flare_ler_baselines.json
+++ /dev/null
@@ -1,207 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flare_ler_baselines",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-82.0177, 29.6878]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, SUGG\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flare_ler_baselines"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flare_ler_baselines"
-    },
-    {
-      "rel": "self",
-      "href": "flare_ler_baselines.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flare_ler_baselines.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flare_ler_baselines.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flare_ler_baselines?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=flare_ler_baselines?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/gem_global.json b/catalog/scores/models/model_items/gem_global.json
deleted file mode 100644
index b457687e34..0000000000
--- a/catalog/scores/models/model_items/gem_global.json
+++ /dev/null
@@ -1,229 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "gem_global",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [37.3032, -79.8372]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: Candian GEM Global Ensemble model downloaded from open-meteo.com\n\nSites: fcre\n\nVariables: Rain_mm_sum, ShortwaveRadiationUp_Wm2_mean, BP_kPa_mean, WindSpeed_ms_mean, AirTemp_C_mean, RH_percent_mean",
-    "start_datetime": "2023-10-14",
-    "end_datetime": "2023-11-30",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "vera4cast",
-      "Rain_mm_sum, ShortwaveRadiationUp_Wm2_mean, BP_kPa_mean, WindSpeed_ms_mean, AirTemp_C_mean, RH_percent_mean"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "depth_m",
-        "type": "double",
-        "description": "depth (meters) in water column of prediction"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "gem_global"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "gem_global"
-    },
-    {
-      "rel": "self",
-      "href": "gem_global.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/gem_global.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/gem_global.json\")\n\n"
-    },
-    "2": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Rain_mm_sum daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=gem_global?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=gem_global?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for ShortwaveRadiationUp_Wm2_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for BP_kPa_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=BP_kPa_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=BP_kPa_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for WindSpeed_ms_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for AirTemp_C_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for RH_percent_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=gem_global?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/gfs_seamless.json b/catalog/scores/models/model_items/gfs_seamless.json
deleted file mode 100644
index 7f7aeb974c..0000000000
--- a/catalog/scores/models/model_items/gfs_seamless.json
+++ /dev/null
@@ -1,229 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "gfs_seamless",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [37.3032, -79.8372]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NOAA Global Ensemble Forecasting Model downloaded using the https://open-meteo.com.  The seamless model combines the 0.25 and 0.5 degree resolution products to get a full 35-day ahead forecast\n\nSites: fcre\n\nVariables: RH_percent_mean, Rain_mm_sum, ShortwaveRadiationUp_Wm2_mean, BP_kPa_mean, AirTemp_C_mean, WindSpeed_ms_mean",
-    "start_datetime": "2023-10-13",
-    "end_datetime": "2023-12-03",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "vera4cast",
-      "RH_percent_mean, Rain_mm_sum, ShortwaveRadiationUp_Wm2_mean, BP_kPa_mean, AirTemp_C_mean, WindSpeed_ms_mean"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "depth_m",
-        "type": "double",
-        "description": "depth (meters) in water column of prediction"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "gfs_seamless"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "gfs_seamless"
-    },
-    {
-      "rel": "self",
-      "href": "gfs_seamless.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/gfs_seamless.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/gfs_seamless.json\")\n\n"
-    },
-    "2": {
-      "type": "application/x-parquet",
-      "title": "Database Access for RH_percent_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Rain_mm_sum daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for ShortwaveRadiationUp_Wm2_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for BP_kPa_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=BP_kPa_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=BP_kPa_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for AirTemp_C_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for WindSpeed_ms_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=gfs_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/glm_aed_v1.json b/catalog/scores/models/model_items/glm_aed_v1.json
deleted file mode 100644
index 701ecb4cc8..0000000000
--- a/catalog/scores/models/model_items/glm_aed_v1.json
+++ /dev/null
@@ -1,247 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "glm_aed_v1",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [37.3032, -79.8372]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: GLM-AED with Ensemble Kalman Filter as implemented in FLARE.  This version used DA to update model states but not model parameters.\n\nSites: fcre\n\nVariables: DIC_mgL_sample, DO_mgL_mean, NH4_ugL_sample, SRP_ugL_sample, Temp_C_mean, fDOM_QSU_mean, Chla_ugL_mean, Secchi_m_sample, Bloom_binary_mean",
-    "start_datetime": "2023-10-14",
-    "end_datetime": "2023-12-01",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "vera4cast",
-      "DIC_mgL_sample, DO_mgL_mean, NH4_ugL_sample, SRP_ugL_sample, Temp_C_mean, fDOM_QSU_mean, Chla_ugL_mean, Secchi_m_sample, Bloom_binary_mean"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "depth_m",
-        "type": "double",
-        "description": "depth (meters) in water column of prediction"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "glm_aed_v1"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "glm_aed_v1"
-    },
-    {
-      "rel": "self",
-      "href": "glm_aed_v1.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/glm_aed_v1.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/glm_aed_v1.json\")\n\n"
-    },
-    "2": {
-      "type": "application/x-parquet",
-      "title": "Database Access for DIC_mgL_sample daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DIC_mgL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DIC_mgL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for DO_mgL_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DO_mgL_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DO_mgL_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for NH4_ugL_sample daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=NH4_ugL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=NH4_ugL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for SRP_ugL_sample daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=SRP_ugL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=SRP_ugL_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Temp_C_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for fDOM_QSU_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=fDOM_QSU_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=fDOM_QSU_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Chla_ugL_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Chla_ugL_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Secchi_m_sample daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Secchi_m_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Secchi_m_sample/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Bloom_binary_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Bloom_binary_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Bloom_binary_mean/model_id=glm_aed_v1?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/icon_seamless.json b/catalog/scores/models/model_items/icon_seamless.json
deleted file mode 100644
index f0d17090c5..0000000000
--- a/catalog/scores/models/model_items/icon_seamless.json
+++ /dev/null
@@ -1,229 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "icon_seamless",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [37.3032, -79.8372]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: The DWD Icon EPS Seamless model downloaded from open-meteo.com\n\nSites: fcre\n\nVariables: BP_kPa_mean, RH_percent_mean, Rain_mm_sum, AirTemp_C_mean, WindSpeed_ms_mean, ShortwaveRadiationUp_Wm2_mean",
-    "start_datetime": "2023-10-14",
-    "end_datetime": "2023-11-05",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "vera4cast",
-      "BP_kPa_mean, RH_percent_mean, Rain_mm_sum, AirTemp_C_mean, WindSpeed_ms_mean, ShortwaveRadiationUp_Wm2_mean"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "depth_m",
-        "type": "double",
-        "description": "depth (meters) in water column of prediction"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "icon_seamless"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "icon_seamless"
-    },
-    {
-      "rel": "self",
-      "href": "icon_seamless.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/icon_seamless.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/icon_seamless.json\")\n\n"
-    },
-    "2": {
-      "type": "application/x-parquet",
-      "title": "Database Access for BP_kPa_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=BP_kPa_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=BP_kPa_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for RH_percent_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=RH_percent_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Rain_mm_sum daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Rain_mm_sum/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for AirTemp_C_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=AirTemp_C_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for WindSpeed_ms_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=WindSpeed_ms_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for ShortwaveRadiationUp_Wm2_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=ShortwaveRadiationUp_Wm2_mean/model_id=icon_seamless?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/inflow_gefsClimAED.json b/catalog/scores/models/model_items/inflow_gefsClimAED.json
deleted file mode 100644
index 261c1883b0..0000000000
--- a/catalog/scores/models/model_items/inflow_gefsClimAED.json
+++ /dev/null
@@ -1,259 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "inflow_gefsClimAED",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [37.3078, -79.8357]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: flow is forecasted as using a linear relationship between historical flow, month, and 5-day sum of precipitation.  Temperature is forecasted using a linear relationship between historical water temperature, month, and 5-day mean air temperature.  NOAA GEFS is then used to get the future values of 5-day sum precipitation and mean temperature.  Nutrients are forecasting using the DOY climatology.  The DOY climatology was developed using a linear interpolation of the historical samples.\n\nSites: tubr\n\nVariables: DIC_mgL_sample, DRSI_mgL_sample, Flow_cms_mean, Temp_C_mean, CH4_umolL_sample, DOC_mgL_sample, TN_ugL_sample, NH4_ugL_sample, TP_ugL_sample, NO3NO2_ugL_sample, SRP_ugL_sample",
-    "start_datetime": "2023-10-13",
-    "end_datetime": "2023-12-03",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "vera4cast",
-      "DIC_mgL_sample, DRSI_mgL_sample, Flow_cms_mean, Temp_C_mean, CH4_umolL_sample, DOC_mgL_sample, TN_ugL_sample, NH4_ugL_sample, TP_ugL_sample, NO3NO2_ugL_sample, SRP_ugL_sample"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "depth_m",
-        "type": "double",
-        "description": "depth (meters) in water column of prediction"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "inflow_gefsClimAED"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "inflow_gefsClimAED"
-    },
-    {
-      "rel": "self",
-      "href": "inflow_gefsClimAED.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/inflow_gefsClimAED.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/inflow_gefsClimAED.json\")\n\n"
-    },
-    "2": {
-      "type": "application/x-parquet",
-      "title": "Database Access for DIC_mgL_sample daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DIC_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DIC_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for DRSI_mgL_sample daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DRSI_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DRSI_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Flow_cms_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Flow_cms_mean/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Flow_cms_mean/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Temp_C_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for CH4_umolL_sample daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=CH4_umolL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=CH4_umolL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for DOC_mgL_sample daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DOC_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=DOC_mgL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for TN_ugL_sample daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=TN_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=TN_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for NH4_ugL_sample daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=NH4_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=NH4_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for TP_ugL_sample daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=TP_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=TP_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for NO3NO2_ugL_sample daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=NO3NO2_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=NO3NO2_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for SRP_ugL_sample daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=SRP_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=SRP_ugL_sample/model_id=inflow_gefsClimAED?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/lasso.json b/catalog/scores/models/model_items/lasso.json
deleted file mode 100644
index 55273cd790..0000000000
--- a/catalog/scores/models/model_items/lasso.json
+++ /dev/null
@@ -1,259 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "lasso",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-09",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "lasso"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "lasso"
-    },
-    {
-      "rel": "self",
-      "href": "lasso.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/lasso.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/lasso.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/mean.json b/catalog/scores/models/model_items/mean.json
deleted file mode 100644
index 827bb4291c..0000000000
--- a/catalog/scores/models/model_items/mean.json
+++ /dev/null
@@ -1,259 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "mean",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-84.2826, 35.9641],
-      [-145.7514, 63.8811],
-      [-149.3705, 68.6611],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-71.2874, 44.0639],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-100.9154, 46.7697],
-      [-81.9934, 29.6893],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-88.1612, 31.8539],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-99.1066, 47.1617],
-      [-104.7456, 40.8155],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-89.5864, 45.5089],
-      [-89.5373, 46.2339],
-      [-78.0418, 39.0337],
-      [-119.2622, 37.0334],
-      [-149.2133, 63.8758],
-      [-78.1395, 38.8929],
-      [-103.0293, 40.4619],
-      [-97.57, 33.4012],
-      [-147.5026, 65.154],
-      [-110.8355, 31.9107]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-20",
-    "end_datetime": "2024-11-25",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Weekly beetle_community_abundance",
-      "Weekly beetle_community_richness"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "mean"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "mean"
-    },
-    {
-      "rel": "self",
-      "href": "mean.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/mean.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/mean.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=mean?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=mean?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=mean?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=mean?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/null.json b/catalog/scores/models/model_items/null.json
deleted file mode 100644
index 20ff4bbed9..0000000000
--- a/catalog/scores/models/model_items/null.json
+++ /dev/null
@@ -1,246 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "null",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-10-10",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "null"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "null"
-    },
-    {
-      "rel": "self",
-      "href": "null.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/null.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/null.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=null?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=null?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=null?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=null?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/persistenceFO.json b/catalog/scores/models/model_items/persistenceFO.json
deleted file mode 100644
index 647f85d83f..0000000000
--- a/catalog/scores/models/model_items/persistenceFO.json
+++ /dev/null
@@ -1,199 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "persistenceFO",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [37.3129, -79.8159]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: another persistence forecast\n\nSites: bvre\n\nVariables: Temp_C_mean",
-    "start_datetime": "2023-09-27",
-    "end_datetime": "2023-10-30",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "vera4cast",
-      "Temp_C_mean"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "depth_m",
-        "type": "double",
-        "description": "depth (meters) in water column of prediction"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "persistenceFO"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "persistenceFO"
-    },
-    {
-      "rel": "self",
-      "href": "persistenceFO.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/persistenceFO.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/persistenceFO.json\")\n\n"
-    },
-    "2": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Temp_C_mean daily",
-      "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=persistenceFO?endpoint_override=renc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230121-bucket01/vera4cast/scores/parquet/duration=P1D/variable=Temp_C_mean/model_id=persistenceFO?endpoint_override=renc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/persistenceRW.json b/catalog/scores/models/model_items/persistenceRW.json
index 538cc95c46..de73b6f9b6 100644
--- a/catalog/scores/models/model_items/persistenceRW.json
+++ b/catalog/scores/models/model_items/persistenceRW.json
@@ -7,102 +7,31 @@
   "id": "persistenceRW",
   "bbox": [
     [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
+      -122.6692,
+      45.5175,
+      -74.7781,
+      45.5175
     ]
   ],
   "geometry": {
     "type": "MultiPoint",
     "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      []
     ]
   },
   "properties": {
-    "description": ["\nmodel info: Random walk from the fable package with ensembles used to represent uncertainty\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature", "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature"],
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2023-12-15",
+    "description": "\nmodel info: Random walk model based on most recent stream chl-a observations using the fable::RW() model.\n\nSites: USGS-01427510, USGS-01463500, USGS-05543010, USGS-05549500, USGS-05553700, USGS-05558300, USGS-05586300, USGS-14181500, USGS-14211010, USGS-14211720\n\nVariables: Daily Chlorophyll_a",
+    "start_datetime": "2024-02-07",
+    "end_datetime": "2024-02-09",
     "providers": [
       {
         "url": "pending",
@@ -124,13 +53,8 @@
     "license": "CC0-1.0",
     "keywords": [
       "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature"
+      "usgsrc4cast",
+      "Daily Chlorophyll_a"
     ],
     "table:columns": [
       {
@@ -284,38 +208,8 @@
     "3": {
       "type": "application/x-parquet",
       "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "href": "s3://anonymous@bio230014-bucket01/challenges/scoresproject_id=/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
+      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/scoresproject_id=/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     }
   }
 }
diff --git a/catalog/scores/models/model_items/procBlanchardMonod.json b/catalog/scores/models/model_items/procBlanchardMonod.json
deleted file mode 100644
index 944c10a2eb..0000000000
--- a/catalog/scores/models/model_items/procBlanchardMonod.json
+++ /dev/null
@@ -1,212 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procBlanchardMonod",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2023-11-30",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procBlanchardMonod"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procBlanchardMonod"
-    },
-    {
-      "rel": "self",
-      "href": "procBlanchardMonod.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procBlanchardMonod.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procBlanchardMonod.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procBlanchardMonod?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procBlanchardMonod?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/procBlanchardSteele.json b/catalog/scores/models/model_items/procBlanchardSteele.json
deleted file mode 100644
index 11e7434e2e..0000000000
--- a/catalog/scores/models/model_items/procBlanchardSteele.json
+++ /dev/null
@@ -1,212 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procBlanchardSteele",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2023-11-30",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procBlanchardSteele"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procBlanchardSteele"
-    },
-    {
-      "rel": "self",
-      "href": "procBlanchardSteele.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procBlanchardSteele.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procBlanchardSteele.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procBlanchardSteele?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procBlanchardSteele?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/procCTMIMonod.json b/catalog/scores/models/model_items/procCTMIMonod.json
deleted file mode 100644
index 2e2dc4042e..0000000000
--- a/catalog/scores/models/model_items/procCTMIMonod.json
+++ /dev/null
@@ -1,212 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procCTMIMonod",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2023-11-30",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procCTMIMonod"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procCTMIMonod"
-    },
-    {
-      "rel": "self",
-      "href": "procCTMIMonod.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procCTMIMonod.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procCTMIMonod.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procCTMIMonod?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procCTMIMonod?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/procCTMISteele.json b/catalog/scores/models/model_items/procCTMISteele.json
deleted file mode 100644
index eaa30dd53f..0000000000
--- a/catalog/scores/models/model_items/procCTMISteele.json
+++ /dev/null
@@ -1,212 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procCTMISteele",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2023-11-30",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procCTMISteele"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procCTMISteele"
-    },
-    {
-      "rel": "self",
-      "href": "procCTMISteele.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procCTMISteele.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procCTMISteele.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procCTMISteele?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procCTMISteele?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/procEppleyNorbergMonod.json b/catalog/scores/models/model_items/procEppleyNorbergMonod.json
deleted file mode 100644
index 4a39706c31..0000000000
--- a/catalog/scores/models/model_items/procEppleyNorbergMonod.json
+++ /dev/null
@@ -1,212 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procEppleyNorbergMonod",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2023-11-30",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procEppleyNorbergMonod"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procEppleyNorbergMonod"
-    },
-    {
-      "rel": "self",
-      "href": "procEppleyNorbergMonod.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procEppleyNorbergMonod.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procEppleyNorbergMonod.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procEppleyNorbergMonod?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procEppleyNorbergMonod?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/procEppleyNorbergSteele.json b/catalog/scores/models/model_items/procEppleyNorbergSteele.json
deleted file mode 100644
index 2f7dfb0469..0000000000
--- a/catalog/scores/models/model_items/procEppleyNorbergSteele.json
+++ /dev/null
@@ -1,212 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procEppleyNorbergSteele",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2023-11-30",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procEppleyNorbergSteele"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procEppleyNorbergSteele"
-    },
-    {
-      "rel": "self",
-      "href": "procEppleyNorbergSteele.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procEppleyNorbergSteele.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procEppleyNorbergSteele.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procEppleyNorbergSteele?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procEppleyNorbergSteele?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/procHinshelwoodMonod.json b/catalog/scores/models/model_items/procHinshelwoodMonod.json
deleted file mode 100644
index 57b7b47959..0000000000
--- a/catalog/scores/models/model_items/procHinshelwoodMonod.json
+++ /dev/null
@@ -1,212 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procHinshelwoodMonod",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2023-11-30",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procHinshelwoodMonod"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procHinshelwoodMonod"
-    },
-    {
-      "rel": "self",
-      "href": "procHinshelwoodMonod.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procHinshelwoodMonod.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procHinshelwoodMonod.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procHinshelwoodMonod?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procHinshelwoodMonod?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/procHinshelwoodSteele.json b/catalog/scores/models/model_items/procHinshelwoodSteele.json
deleted file mode 100644
index 326bf89827..0000000000
--- a/catalog/scores/models/model_items/procHinshelwoodSteele.json
+++ /dev/null
@@ -1,212 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procHinshelwoodSteele",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2023-11-30",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procHinshelwoodSteele"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procHinshelwoodSteele"
-    },
-    {
-      "rel": "self",
-      "href": "procHinshelwoodSteele.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procHinshelwoodSteele.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procHinshelwoodSteele.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procHinshelwoodSteele?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=procHinshelwoodSteele?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/prophet_clim_ensemble.json b/catalog/scores/models/model_items/prophet_clim_ensemble.json
deleted file mode 100644
index cd633bf883..0000000000
--- a/catalog/scores/models/model_items/prophet_clim_ensemble.json
+++ /dev/null
@@ -1,247 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "prophet_clim_ensemble",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-122.3303, 45.7624],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Red_chromatic_coordinate"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "prophet_clim_ensemble"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "prophet_clim_ensemble"
-    },
-    {
-      "rel": "self",
-      "href": "prophet_clim_ensemble.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/prophet_clim_ensemble.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/prophet_clim_ensemble.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=prophet_clim_ensemble?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=prophet_clim_ensemble?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/randfor.json b/catalog/scores/models/model_items/randfor.json
deleted file mode 100644
index 1ac2a13ab6..0000000000
--- a/catalog/scores/models/model_items/randfor.json
+++ /dev/null
@@ -1,252 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "randfor",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-09",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily latent_heat_flux"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "randfor"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "randfor"
-    },
-    {
-      "rel": "self",
-      "href": "randfor.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/randfor.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/randfor.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/tg_arima.json b/catalog/scores/models/model_items/tg_arima.json
deleted file mode 100644
index a26531f63d..0000000000
--- a/catalog/scores/models/model_items/tg_arima.json
+++ /dev/null
@@ -1,349 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_arima",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature, Weekly beetle_community_richness, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population",
-    "start_datetime": "2023-01-01",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature",
-      "Weekly beetle_community_richness",
-      "Weekly beetle_community_abundance",
-      "Weekly Amblyomma_americanum_population"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_arima"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_arima"
-    },
-    {
-      "rel": "self",
-      "href": "tg_arima.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_arima.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_arima.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/tg_auto_adam.json b/catalog/scores/models/model_items/tg_auto_adam.json
deleted file mode 100644
index 692ffe66f9..0000000000
--- a/catalog/scores/models/model_items/tg_auto_adam.json
+++ /dev/null
@@ -1,349 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_auto_adam",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-01-01",
-    "end_datetime": "2023-12-09",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature",
-      "Weekly beetle_community_richness",
-      "Weekly beetle_community_abundance",
-      "Weekly Amblyomma_americanum_population"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_auto_adam"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_auto_adam"
-    },
-    {
-      "rel": "self",
-      "href": "tg_auto_adam.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_auto_adam.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_auto_adam.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/tg_bag_mlp.json b/catalog/scores/models/model_items/tg_bag_mlp.json
deleted file mode 100644
index cb4611979c..0000000000
--- a/catalog/scores/models/model_items/tg_bag_mlp.json
+++ /dev/null
@@ -1,328 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_bag_mlp",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_bag_mlp"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_bag_mlp"
-    },
-    {
-      "rel": "self",
-      "href": "tg_bag_mlp.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_bag_mlp.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_bag_mlp.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/tg_ets.json b/catalog/scores/models/model_items/tg_ets.json
deleted file mode 100644
index bdc0cd6286..0000000000
--- a/catalog/scores/models/model_items/tg_ets.json
+++ /dev/null
@@ -1,349 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_ets",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature, Weekly beetle_community_richness, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population",
-    "start_datetime": "2023-01-01",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature",
-      "Weekly beetle_community_richness",
-      "Weekly beetle_community_abundance",
-      "Weekly Amblyomma_americanum_population"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_ets"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_ets"
-    },
-    {
-      "rel": "self",
-      "href": "tg_ets.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_ets.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_ets.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/tg_humidity_lm.json b/catalog/scores/models/model_items/tg_humidity_lm.json
deleted file mode 100644
index e41779f37a..0000000000
--- a/catalog/scores/models/model_items/tg_humidity_lm.json
+++ /dev/null
@@ -1,328 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_humidity_lm",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature, Daily Net_ecosystem_exchange",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily latent_heat_flux",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature",
-      "Daily Net_ecosystem_exchange"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_humidity_lm"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_humidity_lm"
-    },
-    {
-      "rel": "self",
-      "href": "tg_humidity_lm.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_humidity_lm.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_humidity_lm.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/tg_humidity_lm_all_sites.json b/catalog/scores/models/model_items/tg_humidity_lm_all_sites.json
deleted file mode 100644
index e0a6237b49..0000000000
--- a/catalog/scores/models/model_items/tg_humidity_lm_all_sites.json
+++ /dev/null
@@ -1,328 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_humidity_lm_all_sites",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_humidity_lm_all_sites"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_humidity_lm_all_sites"
-    },
-    {
-      "rel": "self",
-      "href": "tg_humidity_lm_all_sites.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_humidity_lm_all_sites.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_humidity_lm_all_sites.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/tg_lasso.json b/catalog/scores/models/model_items/tg_lasso.json
deleted file mode 100644
index 3ad707e59f..0000000000
--- a/catalog/scores/models/model_items/tg_lasso.json
+++ /dev/null
@@ -1,314 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_lasso",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_lasso"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_lasso"
-    },
-    {
-      "rel": "self",
-      "href": "tg_lasso.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_lasso.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_lasso.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/tg_lasso_all_sites.json b/catalog/scores/models/model_items/tg_lasso_all_sites.json
deleted file mode 100644
index 2c6301163f..0000000000
--- a/catalog/scores/models/model_items/tg_lasso_all_sites.json
+++ /dev/null
@@ -1,314 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_lasso_all_sites",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_lasso_all_sites"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_lasso_all_sites"
-    },
-    {
-      "rel": "self",
-      "href": "tg_lasso_all_sites.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_lasso_all_sites.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_lasso_all_sites.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/tg_precip_lm.json b/catalog/scores/models/model_items/tg_precip_lm.json
deleted file mode 100644
index dc4604826a..0000000000
--- a/catalog/scores/models/model_items/tg_precip_lm.json
+++ /dev/null
@@ -1,328 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_precip_lm",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_precip_lm"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_precip_lm"
-    },
-    {
-      "rel": "self",
-      "href": "tg_precip_lm.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_precip_lm.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_precip_lm.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/tg_precip_lm_all_sites.json b/catalog/scores/models/model_items/tg_precip_lm_all_sites.json
deleted file mode 100644
index 7e2cb02d67..0000000000
--- a/catalog/scores/models/model_items/tg_precip_lm_all_sites.json
+++ /dev/null
@@ -1,328 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_precip_lm_all_sites",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_precip_lm_all_sites"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_precip_lm_all_sites"
-    },
-    {
-      "rel": "self",
-      "href": "tg_precip_lm_all_sites.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_precip_lm_all_sites.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_precip_lm_all_sites.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/tg_randfor.json b/catalog/scores/models/model_items/tg_randfor.json
deleted file mode 100644
index ca6f5fd056..0000000000
--- a/catalog/scores/models/model_items/tg_randfor.json
+++ /dev/null
@@ -1,328 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_randfor",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_randfor"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_randfor"
-    },
-    {
-      "rel": "self",
-      "href": "tg_randfor.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_randfor.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_randfor.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/tg_randfor_all_sites.json b/catalog/scores/models/model_items/tg_randfor_all_sites.json
deleted file mode 100644
index 001fb3878f..0000000000
--- a/catalog/scores/models/model_items/tg_randfor_all_sites.json
+++ /dev/null
@@ -1,300 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_randfor_all_sites",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_randfor_all_sites"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_randfor_all_sites"
-    },
-    {
-      "rel": "self",
-      "href": "tg_randfor_all_sites.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_randfor_all_sites.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_randfor_all_sites.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/tg_tbats.json b/catalog/scores/models/model_items/tg_tbats.json
deleted file mode 100644
index d17b34f5fa..0000000000
--- a/catalog/scores/models/model_items/tg_tbats.json
+++ /dev/null
@@ -1,349 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_tbats",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU, ARIK, BIGC\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature, Weekly beetle_community_richness, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population",
-    "start_datetime": "2023-01-01",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature",
-      "Weekly beetle_community_richness",
-      "Weekly beetle_community_abundance",
-      "Weekly Amblyomma_americanum_population"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_tbats"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_tbats"
-    },
-    {
-      "rel": "self",
-      "href": "tg_tbats.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_tbats.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_tbats.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=richness/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=abundance/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/tg_temp_lm.json b/catalog/scores/models/model_items/tg_temp_lm.json
deleted file mode 100644
index e226e24146..0000000000
--- a/catalog/scores/models/model_items/tg_temp_lm.json
+++ /dev/null
@@ -1,328 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_temp_lm",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_temp_lm"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_temp_lm"
-    },
-    {
-      "rel": "self",
-      "href": "tg_temp_lm.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_temp_lm.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_temp_lm.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/tg_temp_lm_all_sites.json b/catalog/scores/models/model_items/tg_temp_lm_all_sites.json
deleted file mode 100644
index ab2682ebcc..0000000000
--- a/catalog/scores/models/model_items/tg_temp_lm_all_sites.json
+++ /dev/null
@@ -1,328 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_temp_lm_all_sites",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
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-  "geometry": {
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-    "coordinates": [
-      [-82.0084, 29.676],
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-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_temp_lm_all_sites"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_temp_lm_all_sites"
-    },
-    {
-      "rel": "self",
-      "href": "tg_temp_lm_all_sites.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "pending",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_temp_lm_all_sites.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_temp_lm_all_sites.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "pending",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/tslm.json b/catalog/scores/models/model_items/tslm.json
deleted file mode 100644
index b28605cd65..0000000000
--- a/catalog/scores/models/model_items/tslm.json
+++ /dev/null
@@ -1,234 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tslm",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-09-15",
-    "end_datetime": "2023-10-20",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature",
-      "Daily Dissolved_oxygen"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tslm"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tslm"
-    },
-    {
-      "rel": "self",
-      "href": "tslm.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tslm.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tslm.json\")\n\n"
-    },
-    "2": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tslm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=tslm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tslm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=tslm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/scores/models/model_items/xgboost_parallel.json b/catalog/scores/models/model_items/xgboost_parallel.json
deleted file mode 100644
index 4eb7bbc7fd..0000000000
--- a/catalog/scores/models/model_items/xgboost_parallel.json
+++ /dev/null
@@ -1,291 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "xgboost_parallel",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-110.5391, 44.9535],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-10-19",
-    "end_datetime": "2023-11-22",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Net_ecosystem_exchange",
-      "Daily latent_heat_flux",
-      "Daily Dissolved_oxygen",
-      "Daily Green_chromatic_coordinate",
-      "Daily Water_temperature",
-      "Daily Red_chromatic_coordinate",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model."
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.” For summary statistics: “summary.”If this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)."
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "observation",
-        "type": "double",
-        "description": "observed value for variable"
-      },
-      {
-        "name": "crps",
-        "type": "double",
-        "description": "crps forecast score"
-      },
-      {
-        "name": "logs",
-        "type": "double",
-        "description": "logs forecast score"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique project identifier"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "date",
-        "type": "string",
-        "description": "ISO 8601 (ISO 2019) date of the predicted value; follows CF convention http://cfconventions.org/cf-conventions/cf-conventions.html#time-coordinate. This variable was called time before v0.5of the EFI convention. For time-integrated variables (e.g., cumulative net primary productivity), one should specify the start_datetime and end_datetime as two variables, instead of the single datetime. If this is not provided the datetime is assumed to be the MIDPOINT of the integration period."
-      }
-    ]
-  },
-  "collection": "scores",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "xgboost_parallel"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "xgboost_parallel"
-    },
-    {
-      "rel": "self",
-      "href": "xgboost_parallel.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/xgboost_parallel.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/xgboost_parallel.json\")\n\n"
-    },
-    "2": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=nee/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=le/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=oxygen/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=gcc_90/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=temperature/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=rcc_90/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/scores/parquet/duration=P1D/variable=chla/model_id=xgboost_parallel?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/sites/build_sites_page.R b/catalog/sites/build_sites_page.R
index 99e7a08300..0159b8d9d9 100644
--- a/catalog/sites/build_sites_page.R
+++ b/catalog/sites/build_sites_page.R
@@ -8,60 +8,17 @@ config <- yaml::read_yaml('challenge_configuration.yaml')
 catalog_config <- config$catalog_config
 
 ## CREATE table for column descriptions
-site_description_create <- data.frame(field_domain_id = 'domain identifier',
-                                      field_site_id = 'site identifier',
-                                      field_site_name = 'site name',
-                                      terrestrial = 'binary indicator for variable group',
-                                      aquatics = 'binary indicator for variable group',
-                                      phenology = 'binary indicator for variable group',
-                                      ticks = 'binary indicator for variable group',
-                                      beetles = 'binary indicator for variable group',
-                                      phenocam_code = 'code used for phenocam',
-                                      phenocam_roi = 'phenocam region of interest',
-                                      phenocam_vegetation = 'phenocam vegetation type',
-                                      field_site_type = 'site type',
-                                      field_site_subtype = 'site subtype',
-                                      field_colocated_site = 'sites that are colocated',
-                                      field_site_host = 'host site',
-                                      field_site_url = 'host site url',
-                                      field_nonneon_research_allowed = 'is non-neon research allowed',
-                                      field_access_details = 'details for accessing the site',
-                                      field_neon_field_operations_office = 'operations office for neon site',
+site_description_create <- data.frame(site_id = 'site identifier',
+                                      project_id = 'forecast challenge identifier',
+                                      agency_cd = 'organization / agency responsible for site monitoring',
+                                      site_no = 'National Water Information System stream gage identifier',
+                                      station_nm = 'National Water Information System station long name',
+                                      site_tp_cd = 'National Water Information System site type code; https://maps.waterdata.usgs.gov/mapper/help/sitetype.html',
                                       latitude = 'site latitude',
                                       longitude = 'site longitude',
-                                      field_geodetic_datum = 'geodetic coordinates for site',
-                                      field_utm_northing = 'northing utm for site',
-                                      field_utm_easting = 'easting utm for site',
-                                      field_utm_zone = 'utm zone for site',
-                                      field_site_county = 'county where site is located',
-                                      field_site_state = 'state where site is located',
-                                      field_site_country = 'country where site is located',
-                                      field_mean_elevation_m = 'mean site elevation in meters',
-                                      field_minimum_elevation_m = 'minimum site elevation in meters',
-                                      field_maximum_elevation_m = 'maximum site elevation in meters',
-                                      field_mean_annual_temperature_C = 'annual temperature of site in degrees C',
-                                      field_mean_annual_precipitation_mm = 'mean annual precipitation at site in mm',
-                                      field_dominant_wind_direction = 'dominant wind direction at site',
-                                      field_mean_canopy_height_m = 'mean canopy height at site',
-                                      field_dominant_nlcd_classes = 'dominant nlcd classes at site',
-                                      field_domint_plant_species = 'dominant plant species at site',
-                                      field_usgs_huc = 'USGS Hyrdrologic Unit Code (HUC) for site',
-                                      field_watershed_name = 'watershed name for site',
-                                      field_watershed_size_km2 = 'watershed size for site in square kilometers',
-                                      field_lake_depth_mean_m = 'mean lake depth at site in meters',
-                                      field_lake_depth_max_m = 'maximum lake depth in meters',
-                                      field_tower_height_m = 'tower height at site in meters',
-                                      field_usgs_geology_unit = 'USGS geology unit for site',
-                                      field_megapit_soil_family = 'magapit soil family at site',
-                                      field_soil_subgroup = 'soil subgroup at site',
-                                      field_avg_number_of_green_days = 'average number of green days at site',
-                                      field_avg_grean_increase_doy = 'average green increase for day of year',
-                                      field_avg_green_max_doy = 'average green maximum for day of year',
-                                      field_avg_green_decrease_doy = 'average green decrease for day of year',
-                                      field_avg_green_min_doy = 'average green minimum for day of year',
-                                      field_phenocams = 'phenocam details for site',
-                                      field_number_tower_levels = 'number of tower levels at site',
-                                      neon_url = 'neon URL for site')
+                                      site_url = 'National Water Information System URL for monitoring site',
+                                      colocated = '', # TODO: what is colocated?
+                                      queryTime = 'timestamp when site metadata was retrieved')
 
 #inventory_theme_df <- arrow::open_dataset(glue::glue("s3://{config$inventory_bucket}/catalog/forecasts/project_id={config$project_id}"), endpoint_override = config$endpoint, anonymous = TRUE) #|>
 
diff --git a/catalog/sites/collection.json b/catalog/sites/collection.json
index da4018b415..87b0bc4a30 100644
--- a/catalog/sites/collection.json
+++ b/catalog/sites/collection.json
@@ -1,6 +1,6 @@
 {
   "id": "sites",
-  "description": "The catalog contains site metadata for the NEON Ecological Forecasting Challenge",
+  "description": "The catalog contains site metadata for the EFI-USGS River Chlorophyll Forecasting Challenge",
   "stac_version": "1.0.0",
   "license": "CC0-1.0",
   "stac_extensions": [
@@ -31,14 +31,14 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
@@ -47,10 +47,10 @@
     "spatial": {
       "bbox": [
         [
-          -156.6194,
-          17.9696,
-          -66.7987,
-          71.2824
+          -122.6692,
+          39.6327,
+          -74.7781,
+          45.5175
         ]
       ]
     },
@@ -65,99 +65,34 @@
   },
   "table:columns": [
     {
-      "name": "field_domain_id",
-      "type": "character",
-      "description": "domain identifier"
-    },
-    {
-      "name": "field_site_id",
+      "name": "site_id",
       "type": "character",
       "description": "site identifier"
     },
     {
-      "name": "field_site_name",
-      "type": "character",
-      "description": "site name"
-    },
-    {
-      "name": "terrestrial",
-      "type": "numeric",
-      "description": "binary indicator for variable group"
-    },
-    {
-      "name": "aquatics",
-      "type": "numeric",
-      "description": "binary indicator for variable group"
-    },
-    {
-      "name": "phenology",
-      "type": "numeric",
-      "description": "binary indicator for variable group"
-    },
-    {
-      "name": "ticks",
-      "type": "numeric",
-      "description": "binary indicator for variable group"
-    },
-    {
-      "name": "beetles",
-      "type": "numeric",
-      "description": "binary indicator for variable group"
-    },
-    {
-      "name": "phenocam_code",
-      "type": "character",
-      "description": "code used for phenocam"
-    },
-    {
-      "name": "phenocam_roi",
-      "type": "character",
-      "description": "phenocam region of interest"
-    },
-    {
-      "name": "phenocam_vegetation",
-      "type": "character",
-      "description": "phenocam vegetation type"
-    },
-    {
-      "name": "field_site_type",
-      "type": "character",
-      "description": "site type"
-    },
-    {
-      "name": "field_site_subtype",
+      "name": "project_id",
       "type": "character",
-      "description": "site subtype"
+      "description": "forecast challenge identifier"
     },
     {
-      "name": "field_colocated_site",
+      "name": "agency_cd",
       "type": "character",
-      "description": "sites that are colocated"
+      "description": "organization / agency responsible for site monitoring"
     },
     {
-      "name": "field_site_host",
+      "name": "site_no",
       "type": "character",
-      "description": "host site"
+      "description": "National Water Information System stream gage identifier"
     },
     {
-      "name": "field_site_url",
+      "name": "station_nm",
       "type": "character",
-      "description": "host site url"
+      "description": "National Water Information System station long name"
     },
     {
-      "name": "field_nonneon_research_allowed",
+      "name": "site_tp_cd",
       "type": "character",
-      "description": "is non-neon research allowed"
-    },
-    {
-      "name": "field_access_details",
-      "type": "character",
-      "description": "details for accessing the site"
-    },
-    {
-      "name": "field_neon_field_operations_office",
-      "type": "character",
-      "description": "operations office for neon site"
+      "description": "National Water Information System site type code; https://maps.waterdata.usgs.gov/mapper/help/sitetype.html"
     },
     {
       "name": "latitude",
@@ -170,188 +105,38 @@
       "description": "site longitude"
     },
     {
-      "name": "field_geodetic_datum",
+      "name": "site_url",
       "type": "character",
-      "description": "geodetic coordinates for site"
+      "description": "National Water Information System URL for monitoring site"
     },
     {
-      "name": "field_utm_northing",
-      "type": "numeric",
-      "description": "northing utm for site"
+      "name": "colocated",
+      "type": "logical",
+      "description": ""
     },
     {
-      "name": "field_utm_easting",
-      "type": "numeric",
-      "description": "easting utm for site"
-    },
-    {
-      "name": "field_utm_zone",
-      "type": "character",
-      "description": "utm zone for site"
-    },
-    {
-      "name": "field_site_county",
-      "type": "character",
-      "description": "county where site is located"
-    },
-    {
-      "name": "field_site_state",
-      "type": "character",
-      "description": "state where site is located"
-    },
-    {
-      "name": "field_site_country",
-      "type": "character",
-      "description": "country where site is located"
-    },
-    {
-      "name": "field_mean_elevation_m",
-      "type": "numeric",
-      "description": "mean site elevation in meters"
-    },
-    {
-      "name": "field_minimum_elevation_m",
-      "type": "numeric",
-      "description": "minimum site elevation in meters"
-    },
-    {
-      "name": "field_maximum_elevation_m",
-      "type": "numeric",
-      "description": "maximum site elevation in meters"
-    },
-    {
-      "name": "field_mean_annual_temperature_C",
-      "type": "numeric",
-      "description": "annual temperature of site in degrees C"
-    },
-    {
-      "name": "field_mean_annual_precipitation_mm",
-      "type": "numeric",
-      "description": "mean annual precipitation at site in mm"
-    },
-    {
-      "name": "field_dominant_wind_direction",
-      "type": "character",
-      "description": "dominant wind direction at site"
-    },
-    {
-      "name": "field_mean_canopy_height_m",
-      "type": "character",
-      "description": "mean canopy height at site"
-    },
-    {
-      "name": "field_dominant_nlcd_classes",
-      "type": "character",
-      "description": "dominant nlcd classes at site"
-    },
-    {
-      "name": "field_domint_plant_species",
-      "type": "character",
-      "description": "dominant plant species at site"
-    },
-    {
-      "name": "field_usgs_huc",
-      "type": "character",
-      "description": "USGS Hyrdrologic Unit Code (HUC) for site"
-    },
-    {
-      "name": "field_watershed_name",
-      "type": "character",
-      "description": "watershed name for site"
-    },
-    {
-      "name": "field_watershed_size_km2",
-      "type": "numeric",
-      "description": "watershed size for site in square kilometers"
-    },
-    {
-      "name": "field_lake_depth_mean_m",
-      "type": "numeric",
-      "description": "mean lake depth at site in meters"
-    },
-    {
-      "name": "field_lake_depth_max_m",
-      "type": "numeric",
-      "description": "maximum lake depth in meters"
-    },
-    {
-      "name": "field_tower_height_m",
-      "type": "numeric",
-      "description": "tower height at site in meters"
-    },
-    {
-      "name": "field_usgs_geology_unit",
-      "type": "character",
-      "description": "USGS geology unit for site"
-    },
-    {
-      "name": "field_megapit_soil_family",
-      "type": "character",
-      "description": "magapit soil family at site"
-    },
-    {
-      "name": "field_soil_subgroup",
-      "type": "character",
-      "description": "soil subgroup at site"
-    },
-    {
-      "name": "field_avg_number_of_green_days",
-      "type": "numeric",
-      "description": "average number of green days at site"
-    },
-    {
-      "name": "field_avg_grean_increase_doy",
-      "type": "character",
-      "description": "average green increase for day of year"
-    },
-    {
-      "name": "field_avg_green_max_doy",
-      "type": "character",
-      "description": "average green maximum for day of year"
-    },
-    {
-      "name": "field_avg_green_decrease_doy",
-      "type": "character",
-      "description": "average green decrease for day of year"
-    },
-    {
-      "name": "field_avg_green_min_doy",
-      "type": "character",
-      "description": "average green minimum for day of year"
-    },
-    {
-      "name": "field_phenocams",
-      "type": "character",
-      "description": "phenocam details for site"
-    },
-    {
-      "name": "field_number_tower_levels",
-      "type": "numeric",
-      "description": "number of tower levels at site"
-    },
-    {
-      "name": "neon_url",
-      "type": "character",
-      "description": "neon URL for site"
+      "name": "queryTime",
+      "type": ["POSIXct", "POSIXt"],
+      "description": "timestamp when site metadata was retrieved"
     }
   ],
   "assets": {
     "data": {
-      "href": "https://raw.githubusercontent.com/eco4cast/neon4cast-ci/main/neon4cast_field_site_metadata.csv",
+      "href": "https://raw.githubusercontent.com/eco4cast/usgsrc4cast-ci/main/USGS_site_metadata.csv",
       "type": "application/x-parquet",
       "title": "Site Metadata Access",
       "roles": [
         "data"
       ],
-      "description": "This R code will return results for the site metadata.\n\n### R\n\n```{r}\n# Use code below\n\nurl <- https://raw.githubusercontent.com/eco4cast/neon4cast-ci/main/neon4cast_field_site_metadata.csv\nsites <- readr::read_csv(url, show_col_types = FALSE)\n```"
+      "description": "This R code will return results for the site metadata.\n\n### R\n\n```{r}\n# Use code below\n\nurl <- https://raw.githubusercontent.com/eco4cast/usgsrc4cast-ci/main/USGS_site_metadata.csv\nsites <- readr::read_csv(url, show_col_types = FALSE)\n```"
     },
     "thumbnail": {
-      "href": "https://www.neonscience.org/sites/default/files/styles/max_2600x2600/public/2021-04/2021_04_graphic_Domain_Map_no-Titles-png.png?itok=7MsHPigZ",
+      "href": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/thumbnails/image/screencapture-waterdata-usgs-gov-nwis-rt-2018-08-02-13_00_05-01.jpg",
       "type": "image/JPEG",
       "roles": [
         "thumbnail"
       ],
-      "title": "NEON Sites Map"
+      "title": "USGS Sites Map"
     }
   }
 }
diff --git a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json b/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json
deleted file mode 100644
index 1f8c46088e..0000000000
--- a/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json
+++ /dev/null
@@ -1,277 +0,0 @@
-{
-  "id": "Daily_Dissolved_oxygen",
-  "description": "This page includes all models for the Daily_Dissolved_oxygen variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/GLEON_lm_lag_1day.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/air2waterSat_2.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_f1.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_prophet.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/climatology.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/persistenceRW.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/null.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_bag_mlp.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Daily_Dissolved_oxygen",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-149.6106, 18.1135, -66.7987, 68.6698]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-01T00:00:00Z",
-          "2024-11-10T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique identifier for the forecast project"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "reference_date",
-      "type": "string",
-      "description": "date that the forecast was initiated"
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=oxygen?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=oxygen?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/summaries/Aquatics/Daily_Water_temperature/collection.json b/catalog/summaries/Aquatics/Daily_Water_temperature/collection.json
deleted file mode 100644
index d50a1b6ac5..0000000000
--- a/catalog/summaries/Aquatics/Daily_Water_temperature/collection.json
+++ /dev/null
@@ -1,347 +0,0 @@
-{
-  "id": "Daily_Water_temperature",
-  "description": "This page includes all models for the Daily_Water_temperature variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_bag_mlp.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/GLEON_JRabaey_temp_physics.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/GLEON_lm_lag_1day.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/GLEON_physics.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/air2waterSat_2.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/baseline_ensemble.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_f1.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_prophet.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/climatology.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/fARIMA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/fARIMA_clim_ensemble.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/fTSLM_lag.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flareGLM.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flareGLM_noDA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flareGOTM.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flareGOTM_noDA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flareSimstrat.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flareSimstrat_noDA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flare_ler.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/flare_ler_baselines.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/persistenceRW.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/null.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Daily_Water_temperature",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-149.6106, 18.1135, -66.7987, 68.6698]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-01T00:00:00Z",
-          "2024-11-10T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique identifier for the forecast project"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "reference_date",
-      "type": "string",
-      "description": "date that the forecast was initiated"
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=temperature?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=temperature?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/collection.json b/catalog/summaries/Beetles/Weekly_beetle_community_abundance/collection.json
deleted file mode 100644
index dd94d54f40..0000000000
--- a/catalog/summaries/Beetles/Weekly_beetle_community_abundance/collection.json
+++ /dev/null
@@ -1,242 +0,0 @@
-{
-  "id": "Weekly_beetle_community_abundance",
-  "description": "This page includes all models for the Weekly_beetle_community_abundance variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/mean.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Weekly_beetle_community_abundance",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-02T00:00:00Z",
-          "2024-12-09T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique identifier for the forecast project"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "reference_date",
-      "type": "string",
-      "description": "date that the forecast was initiated"
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1W/variable=abundance?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1W/variable=abundance?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/summaries/Beetles/Weekly_beetle_community_richness/collection.json b/catalog/summaries/Beetles/Weekly_beetle_community_richness/collection.json
deleted file mode 100644
index d36812650d..0000000000
--- a/catalog/summaries/Beetles/Weekly_beetle_community_richness/collection.json
+++ /dev/null
@@ -1,242 +0,0 @@
-{
-  "id": "Weekly_beetle_community_richness",
-  "description": "This page includes all models for the Weekly_beetle_community_richness variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/mean.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Weekly_beetle_community_richness",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-02T00:00:00Z",
-          "2024-12-09T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique identifier for the forecast project"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "reference_date",
-      "type": "string",
-      "description": "date that the forecast was initiated"
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1W/variable=richness?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1W/variable=richness?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json b/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json
deleted file mode 100644
index f35929fe7c..0000000000
--- a/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json
+++ /dev/null
@@ -1,252 +0,0 @@
-{
-  "id": "Daily_Green_chromatic_coordinate",
-  "description": "This page includes all models for the Daily_Green_chromatic_coordinate variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_prophet.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/climatology.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/persistenceRW.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_bag_mlp.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Daily_Green_chromatic_coordinate",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-01T00:00:00Z",
-          "2024-01-21T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique identifier for the forecast project"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "reference_date",
-      "type": "string",
-      "description": "date that the forecast was initiated"
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=gcc_90?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=gcc_90?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json b/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json
deleted file mode 100644
index 069c93992a..0000000000
--- a/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json
+++ /dev/null
@@ -1,267 +0,0 @@
-{
-  "id": "Daily_Red_chromatic_coordinate",
-  "description": "This page includes all models for the Daily_Red_chromatic_coordinate variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_prophet.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/climatology.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/persistenceRW.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/prophet_clim_ensemble.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_bag_mlp.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/baseline_ensemble.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Daily_Red_chromatic_coordinate",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-01T00:00:00Z",
-          "2024-01-21T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique identifier for the forecast project"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "reference_date",
-      "type": "string",
-      "description": "date that the forecast was initiated"
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=rcc_90?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=rcc_90?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/summaries/Terrestrial/30min_Net_ecosystem_exchange/collection.json b/catalog/summaries/Terrestrial/30min_Net_ecosystem_exchange/collection.json
deleted file mode 100644
index 9ba8b5ba9f..0000000000
--- a/catalog/summaries/Terrestrial/30min_Net_ecosystem_exchange/collection.json
+++ /dev/null
@@ -1,252 +0,0 @@
-{
-  "id": "30min_Net_ecosystem_exchange",
-  "description": "This page includes all models for the 30min_Net_ecosystem_exchange variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_prophet.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/climatology.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/persistenceRW.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_bag_mlp.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/USUNEEDAILY.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "30min_Net_ecosystem_exchange",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-01T00:00:00Z",
-          "2024-01-22T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique identifier for the forecast project"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "reference_date",
-      "type": "string",
-      "description": "date that the forecast was initiated"
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=nee?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=nee?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/summaries/Terrestrial/30min_latent_heat_flux/collection.json b/catalog/summaries/Terrestrial/30min_latent_heat_flux/collection.json
deleted file mode 100644
index 6ffb3148b3..0000000000
--- a/catalog/summaries/Terrestrial/30min_latent_heat_flux/collection.json
+++ /dev/null
@@ -1,247 +0,0 @@
-{
-  "id": "30min_latent_heat_flux",
-  "description": "This page includes all models for the 30min_latent_heat_flux variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_prophet.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/climatology.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_bag_mlp.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "30min_latent_heat_flux",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-01T00:00:00Z",
-          "2024-01-22T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique identifier for the forecast project"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "reference_date",
-      "type": "string",
-      "description": "date that the forecast was initiated"
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=le?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=le?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json b/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json
deleted file mode 100644
index 69e42aefe7..0000000000
--- a/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json
+++ /dev/null
@@ -1,252 +0,0 @@
-{
-  "id": "Daily_Net_ecosystem_exchange",
-  "description": "This page includes all models for the Daily_Net_ecosystem_exchange variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_prophet.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/climatology.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/persistenceRW.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_bag_mlp.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/USUNEEDAILY.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Daily_Net_ecosystem_exchange",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-01T00:00:00Z",
-          "2024-01-22T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique identifier for the forecast project"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "reference_date",
-      "type": "string",
-      "description": "date that the forecast was initiated"
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=nee?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=nee?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/collection.json b/catalog/summaries/Terrestrial/Daily_latent_heat_flux/collection.json
deleted file mode 100644
index 6ee353c7a1..0000000000
--- a/catalog/summaries/Terrestrial/Daily_latent_heat_flux/collection.json
+++ /dev/null
@@ -1,247 +0,0 @@
-{
-  "id": "Daily_latent_heat_flux",
-  "description": "This page includes all models for the Daily_latent_heat_flux variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/cb_prophet.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/climatology.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_bag_mlp.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Daily_latent_heat_flux",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-01T00:00:00Z",
-          "2024-01-22T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique identifier for the forecast project"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "reference_date",
-      "type": "string",
-      "description": "date that the forecast was initiated"
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=le?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1D/variable=le?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/collection.json b/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/collection.json
deleted file mode 100644
index fdae2fd069..0000000000
--- a/catalog/summaries/Ticks/Weekly_Amblyomma_americanum_population/collection.json
+++ /dev/null
@@ -1,237 +0,0 @@
-{
-  "id": "Weekly_Amblyomma_americanum_population",
-  "description": "This page includes all models for the Weekly_Amblyomma_americanum_population variable.",
-  "stac_version": "1.0.0",
-  "license": "CC0-1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Collection",
-  "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_randfor_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_lasso_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "../../models/model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "parent",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "root",
-      "type": "application/json",
-      "href": "../collection.json"
-    },
-    {
-      "rel": "self",
-      "type": "application/json",
-      "href": "collection.json"
-    },
-    {
-      "rel": "cite-as",
-      "href": "https://doi.org/10.1002/fee.2616"
-    },
-    {
-      "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
-    },
-    {
-      "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
-      "type": "text/html"
-    }
-  ],
-  "title": "Weekly_Amblyomma_americanum_population",
-  "extent": {
-    "spatial": {
-      "bbox": [
-        [-96.5631, 29.6893, -76.56, 39.1008]
-      ]
-    },
-    "temporal": {
-      "interval": [
-        [
-          "2023-01-02T00:00:00Z",
-          "2024-12-09T00:00:00Z"
-        ]
-      ]
-    }
-  },
-  "table:columns": [
-    {
-      "name": "reference_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that the forecast was initiated (horizon = 0)"
-    },
-    {
-      "name": "site_id",
-      "type": "string",
-      "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-    },
-    {
-      "name": "datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime of the forecasted value (ISO 8601)"
-    },
-    {
-      "name": "family",
-      "type": "string",
-      "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-    },
-    {
-      "name": "pub_datetime",
-      "type": "timestamp[us, tz=UTC]",
-      "description": "datetime that forecast was submitted"
-    },
-    {
-      "name": "mean",
-      "type": "double",
-      "description": "mean forecast prediction"
-    },
-    {
-      "name": "median",
-      "type": "double",
-      "description": "median forecast prediction"
-    },
-    {
-      "name": "sd",
-      "type": "double",
-      "description": "standard deviation forecasts"
-    },
-    {
-      "name": "quantile97.5",
-      "type": "double",
-      "description": "upper 97.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile02.5",
-      "type": "double",
-      "description": "upper 2.5 percentile value of forecast"
-    },
-    {
-      "name": "quantile90",
-      "type": "double",
-      "description": "upper 90 percentile value of forecast"
-    },
-    {
-      "name": "quantile10",
-      "type": "double",
-      "description": "upper 10 percentile value of forecast"
-    },
-    {
-      "name": "project_id",
-      "type": "string",
-      "description": "unique identifier for the forecast project"
-    },
-    {
-      "name": "duration",
-      "type": "string",
-      "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-    },
-    {
-      "name": "variable",
-      "type": "string",
-      "description": "name of forecasted variable"
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "unique model identifier"
-    },
-    {
-      "name": "reference_date",
-      "type": "string",
-      "description": "date that the forecast was initiated"
-    }
-  ],
-  "assets": {
-    "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum?endpoint_override=sdsc.osn.xsede.org\"",
-      "type": "application/x-parquet",
-      "title": "Database Access",
-      "roles": [
-        "data"
-      ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=neon4cast/duration=P1W/variable=amblyomma_americanum?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "thumbnail": {
-      "href": "pending",
-      "type": "image/JPEG",
-      "roles": [
-        "thumbnail"
-      ],
-      "title": "pending"
-    }
-  }
-}
diff --git a/catalog/summaries/Phenology/collection.json b/catalog/summaries/aquatics/Daily_Chlorophyll_a/collection.json
similarity index 71%
rename from catalog/summaries/Phenology/collection.json
rename to catalog/summaries/aquatics/Daily_Chlorophyll_a/collection.json
index ccfb429056..ff12ff56e7 100644
--- a/catalog/summaries/Phenology/collection.json
+++ b/catalog/summaries/aquatics/Daily_Chlorophyll_a/collection.json
@@ -1,6 +1,6 @@
 {
-  "id": "Phenology",
-  "description": "This page includes variables for the Phenology group.",
+  "id": "Daily_Chlorophyll_a",
+  "description": "This page includes all models for the Daily_Chlorophyll_a variable.",
   "stac_version": "1.0.0",
   "license": "CC0-1.0",
   "stac_extensions": [
@@ -11,14 +11,14 @@
   "type": "Collection",
   "links": [
     {
-      "rel": "child",
+      "rel": "item",
       "type": "application/json",
-      "href": "Daily_Green_chromatic_coordinate/collection.json"
+      "href": "../../models/model_items/climatology.json"
     },
     {
-      "rel": "child",
+      "rel": "item",
       "type": "application/json",
-      "href": "Daily_Red_chromatic_coordinate/collection.json"
+      "href": "../../models/model_items/persistenceRW.json"
     },
     {
       "rel": "parent",
@@ -41,29 +41,29 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
-  "title": "Phenology",
+  "title": "Daily_Chlorophyll_a",
   "extent": {
     "spatial": {
       "bbox": [
-        [-156.6194, 17.9696, -66.8687, 71.2824]
+        ["Inf", "Inf", "-Inf", "-Inf"]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2023-01-01T00:00:00Z",
-          "2024-12-09T00:00:00Z"
+          "2024-02-07T00:00:00Z",
+          "2024-03-14T00:00:00Z"
         ]
       ]
     }
@@ -157,21 +157,21 @@
   ],
   "assets": {
     "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org\"",
+      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=usgsrc4cast/duration=P1D/variable=chla?endpoint_override=sdsc.osn.xsede.org",
       "type": "application/x-parquet",
       "title": "Database Access",
       "roles": [
         "data"
       ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for the NEON Ecological Forecasting Aquatics theme.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |>\n dplyr::filter(variable %in% c(\"gcc_90\", \"rcc_90\")) |>\n dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `arrow` for remote access to the database. This R code will return results for forecasts of the variable by the specific model .\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/parquet/project_id=usgsrc4cast/duration=P1D/variable=chla?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     },
     "thumbnail": {
-      "href": "https://www.neonscience.org/sites/default/files/styles/max_1300x1300/public/image-content-images/_BFP8455.jpg",
+      "href": "pending",
       "type": "image/JPEG",
       "roles": [
         "thumbnail"
       ],
-      "title": "Phenology Image"
+      "title": "pending"
     }
   }
 }
diff --git a/catalog/summaries/Ticks/collection.json b/catalog/summaries/aquatics/collection.json
similarity index 78%
rename from catalog/summaries/Ticks/collection.json
rename to catalog/summaries/aquatics/collection.json
index a9d0f4d635..c2361a337e 100644
--- a/catalog/summaries/Ticks/collection.json
+++ b/catalog/summaries/aquatics/collection.json
@@ -1,6 +1,6 @@
 {
-  "id": "Ticks",
-  "description": "This page includes variables for the Ticks group.",
+  "id": "aquatics",
+  "description": "This page includes variables for the aquatics group.",
   "stac_version": "1.0.0",
   "license": "CC0-1.0",
   "stac_extensions": [
@@ -13,7 +13,7 @@
     {
       "rel": "child",
       "type": "application/json",
-      "href": "Weekly_Amblyomma_americanum_population/collection.json"
+      "href": "Daily_Chlorophyll_a/collection.json"
     },
     {
       "rel": "parent",
@@ -36,29 +36,29 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
-  "title": "Ticks",
+  "title": "aquatics",
   "extent": {
     "spatial": {
       "bbox": [
-        [-96.5631, 29.6893, -76.56, 39.1008]
+        ["Inf", "Inf", "-Inf", "-Inf"]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2023-01-01T00:00:00Z",
-          "2024-12-09T00:00:00Z"
+          "2024-02-07T00:00:00Z",
+          "2024-03-14T00:00:00Z"
         ]
       ]
     }
@@ -152,21 +152,21 @@
   ],
   "assets": {
     "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org\"",
+      "href": "s3://anonymous@bio230014-bucket01/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org",
       "type": "application/x-parquet",
       "title": "Database Access",
       "roles": [
         "data"
       ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for the NEON Ecological Forecasting Aquatics theme.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |>\n dplyr::filter(variable %in% c(\"amblyomma_americanum\")) |>\n dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `arrow` for remote access to the database. This R code will return results for the NEON Ecological Forecasting Aquatics theme.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |>\n dplyr::filter(variable %in% c(\"chla\")) |>\n dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     },
     "thumbnail": {
-      "href": "https://raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/thumbnail_plots/tick_drag.jpg",
+      "href": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/thumbnails/image/Back-b.jpg",
       "type": "image/JPEG",
       "roles": [
         "thumbnail"
       ],
-      "title": "NEON Ticks"
+      "title": "USGS Streamgage"
     }
   }
 }
diff --git a/catalog/summaries/collection.json b/catalog/summaries/collection.json
index ebca609128..332caa159a 100644
--- a/catalog/summaries/collection.json
+++ b/catalog/summaries/collection.json
@@ -13,32 +13,8 @@
     {
       "rel": "child",
       "type": "application/json",
-      "href": "Aquatics/collection.json",
-      "title": "Aquatics"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "Terrestrial/collection.json",
-      "title": "Terrestrial"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "Phenology/collection.json",
-      "title": "Phenology"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "Beetles/collection.json",
-      "title": "Beetles"
-    },
-    {
-      "rel": "child",
-      "type": "application/json",
-      "href": "Ticks/collection.json",
-      "title": "Ticks"
+      "href": "aquatics/collection.json",
+      "title": "aquatics"
     },
     {
       "rel": "child",
@@ -67,14 +43,14 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
@@ -83,18 +59,18 @@
     "spatial": {
       "bbox": [
         [
-          -156.6194,
-          17.9696,
-          -66.7987,
-          71.2824
+          -122.6692,
+          39.6327,
+          -74.7781,
+          45.5175
         ]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2023-01-01T00:00:00Z",
-          "2024-12-09T00:00:00Z"
+          "2024-02-07T00:00:00Z",
+          "2024-03-14T00:00:00Z"
         ]
       ]
     }
@@ -188,21 +164,21 @@
   ],
   "assets": {
     "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org\"",
+      "href": "s3://anonymous@bio230014-bucket01/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org",
       "type": "application/x-parquet",
       "title": "Database Access",
       "roles": [
         "data"
       ],
-      "description": "Use `arrow` for remote access to the database. This R code will return results for the VERA Forecasting Challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "description": "Use `arrow` for remote access to the database. This R code will return results for the Forecasting Challenge.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     },
     "thumbnail": {
-      "href": "https://raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/thumbnail_plots/neon_desert.jpg",
+      "href": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/thumbnails/image/Manual%20measurement%20streamgage.jpg",
       "type": "image/JPEG",
       "roles": [
         "thumbnail"
       ],
-      "title": "NEON Image"
+      "title": "USGS Image"
     }
   }
 }
diff --git a/catalog/summaries/models/collection.json b/catalog/summaries/models/collection.json
index 5f05811eb0..a601aa832b 100644
--- a/catalog/summaries/models/collection.json
+++ b/catalog/summaries/models/collection.json
@@ -10,16 +10,6 @@
   ],
   "type": "Collection",
   "links": [
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/USGSHABs1.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/cb_prophet.json"
-    },
     {
       "rel": "item",
       "type": "application/json",
@@ -30,236 +20,6 @@
       "type": "application/json",
       "href": "model_items/persistenceRW.json"
     },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procBlanchardMonod.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procBlanchardSteele.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procCTMIMonod.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procCTMISteele.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procEppleyNorbergMonod.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procEppleyNorbergSteele.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procHinshelwoodSteele.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_arima.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_bag_mlp.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_ets.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_humidity_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_lasso_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_precip_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_precip_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_tbats.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_temp_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_auto_adam.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_humidity_lm_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_temp_lm.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/lasso.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/randfor.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/prophet_clim_ensemble.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/GLEON_lm_lag_1day.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/air2waterSat_2.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/cb_f1.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/mean.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/tg_randfor_all_sites.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/GLEON_JRabaey_temp_physics.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/GLEON_physics.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/baseline_ensemble.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/fARIMA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/fARIMA_clim_ensemble.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/fTSLM_lag.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flareGLM.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flareGLM_noDA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flareGOTM.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flareGOTM_noDA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flareSimstrat.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flareSimstrat_noDA.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flare_ler.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/flare_ler_baselines.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/null.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/procHinshelwoodMonod.json"
-    },
-    {
-      "rel": "item",
-      "type": "application/json",
-      "href": "model_items/USUNEEDAILY.json"
-    },
     {
       "rel": "parent",
       "type": "application/json",
@@ -281,14 +41,14 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
@@ -296,14 +56,14 @@
   "extent": {
     "spatial": {
       "bbox": [
-        [-156.6194, 17.9696, -66.7987, 71.2824]
+        [-122.6692, 39.6328, -74.7781, 45.5175]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2023-01-01T00:00:00Z",
-          "2024-12-09T00:00:00Z"
+          "2024-02-07T00:00:00Z",
+          "2024-03-14T00:00:00Z"
         ]
       ]
     }
@@ -397,7 +157,7 @@
   ],
   "assets": {
     "data": {
-      "href": "\"s3://anonymous@bio230014-bucket01/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org\"",
+      "href": "s3://anonymous@bio230014-bucket01/vera4cast/forecasts/summaries/parquet/?endpoint_override=sdsc.osn.xsede.org",
       "type": "application/x-parquet",
       "title": "Database Access",
       "roles": [
diff --git a/catalog/summaries/models/model_items/.empty b/catalog/summaries/models/model_items/.empty
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/catalog/summaries/models/model_items/GLEON_JRabaey_temp_physics.json b/catalog/summaries/models/model_items/GLEON_JRabaey_temp_physics.json
deleted file mode 100644
index 6f3aeda12a..0000000000
--- a/catalog/summaries/models/model_items/GLEON_JRabaey_temp_physics.json
+++ /dev/null
@@ -1,224 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "GLEON_JRabaey_temp_physics",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-18",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "GLEON_JRabaey_temp_physics"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "GLEON_JRabaey_temp_physics"
-    },
-    {
-      "rel": "self",
-      "href": "GLEON_JRabaey_temp_physics.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_JRabaey_temp_physics.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_JRabaey_temp_physics.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=GLEON_JRabaey_temp_physics?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=GLEON_JRabaey_temp_physics?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/GLEON_lm_lag_1day.json b/catalog/summaries/models/model_items/GLEON_lm_lag_1day.json
deleted file mode 100644
index 8442f9f9c0..0000000000
--- a/catalog/summaries/models/model_items/GLEON_lm_lag_1day.json
+++ /dev/null
@@ -1,204 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "GLEON_lm_lag_1day",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Dissolved_oxygen, Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "GLEON_lm_lag_1day"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "GLEON_lm_lag_1day"
-    },
-    {
-      "rel": "self",
-      "href": "GLEON_lm_lag_1day.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_lm_lag_1day.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_lm_lag_1day.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=GLEON_lm_lag_1day?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/GLEON_physics.json b/catalog/summaries/models/model_items/GLEON_physics.json
deleted file mode 100644
index 76d11169d5..0000000000
--- a/catalog/summaries/models/model_items/GLEON_physics.json
+++ /dev/null
@@ -1,196 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "GLEON_physics",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2023-12-22",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "GLEON_physics"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "GLEON_physics"
-    },
-    {
-      "rel": "self",
-      "href": "GLEON_physics.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_physics.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/GLEON_physics.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=GLEON_physics?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=GLEON_physics?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/USGSHABs1.json b/catalog/summaries/models/model_items/USGSHABs1.json
deleted file mode 100644
index 0ad47c9be8..0000000000
--- a/catalog/summaries/models/model_items/USGSHABs1.json
+++ /dev/null
@@ -1,193 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "USGSHABs1",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-87.7982, 32.5415],
-      [-88.1589, 31.8534],
-      [-84.4374, 31.1854]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BLWA, TOMB, FLNT\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-12",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "USGSHABs1"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "USGSHABs1"
-    },
-    {
-      "rel": "self",
-      "href": "USGSHABs1.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/USGSHABs1.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/USGSHABs1.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=USGSHABs1?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=USGSHABs1?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/USUNEEDAILY.json b/catalog/summaries/models/model_items/USUNEEDAILY.json
deleted file mode 100644
index 7693f94820..0000000000
--- a/catalog/summaries/models/model_items/USUNEEDAILY.json
+++ /dev/null
@@ -1,191 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "USUNEEDAILY",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-155.3173, 19.5531]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: \"Home brew ARIMA.\" We didn't use a formal time series framework because of all the missing values in both our response variable and the weather covariates. So we used a GAM to fit a seasonal component based on day of year, and we included NEE the previous day as as an AR 1 term. We did some model selection, using cross validation, to identify temperature and relative humidity as weather covariates.\n\nSites: PUUM\n\nVariables: Daily Net_ecosystem_exchange",
-    "start_datetime": "2023-12-12",
-    "end_datetime": "2024-01-16",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Net_ecosystem_exchange"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "USUNEEDAILY"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "USUNEEDAILY"
-    },
-    {
-      "rel": "self",
-      "href": "USUNEEDAILY.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": "https://drive.google.com/file/d/10uvb3HWR0nHOHrBSQTTPc9vZnYgFgbVa/view?usp=sharing",
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/USUNEEDAILY.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/USUNEEDAILY.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": "https://drive.google.com/file/d/10uvb3HWR0nHOHrBSQTTPc9vZnYgFgbVa/view?usp=sharing",
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=USUNEEDAILY?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=USUNEEDAILY?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/air2waterSat_2.json b/catalog/summaries/models/model_items/air2waterSat_2.json
deleted file mode 100644
index 061ac8098d..0000000000
--- a/catalog/summaries/models/model_items/air2waterSat_2.json
+++ /dev/null
@@ -1,231 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "air2waterSat_2",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, TOOK, WALK, WLOU\n\nVariables: Daily Dissolved_oxygen, Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-20",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "air2waterSat_2"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "air2waterSat_2"
-    },
-    {
-      "rel": "self",
-      "href": "air2waterSat_2.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/air2waterSat_2.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/air2waterSat_2.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=air2waterSat_2?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/baseline_ensemble.json b/catalog/summaries/models/model_items/baseline_ensemble.json
deleted file mode 100644
index d034b4133b..0000000000
--- a/catalog/summaries/models/model_items/baseline_ensemble.json
+++ /dev/null
@@ -1,266 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "baseline_ensemble",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-87.7982, 32.5415],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-72.1727, 42.5369],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-99.1066, 47.1617],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-122.3303, 45.7624],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BLWA, COMO, CUPE, FLNT, GUIL, HOPB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, MCDI, MCRA, POSE, PRIN, REDB, ARIK, BARC, BIGC, BLDE, BLUE, KING, LECO, LEWI, MART, MAYF, LENO, MLBS, MOAB, NIWO, NOGP, OAES, HARV, JERC, JORN, KONA, KONZ, LAJA, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, SCBI, SERC, SJER, SOAP, SRER, ONAQ, ORNL, OSBS, PUUM, RMNP, DCFS, DELA, DSNY, GRSM, GUAN, ABBY, BART, BLAN, CLBJ, CPER\n\nVariables: Daily Water_temperature, Daily Red_chromatic_coordinate",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature",
-      "Daily Red_chromatic_coordinate"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "baseline_ensemble"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "baseline_ensemble"
-    },
-    {
-      "rel": "self",
-      "href": "baseline_ensemble.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/baseline_ensemble.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/baseline_ensemble.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=baseline_ensemble?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/cb_f1.json b/catalog/summaries/models/model_items/cb_f1.json
deleted file mode 100644
index c702738d65..0000000000
--- a/catalog/summaries/models/model_items/cb_f1.json
+++ /dev/null
@@ -1,231 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "cb_f1",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-10-11",
-    "end_datetime": "2024-11-10",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "cb_f1"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "cb_f1"
-    },
-    {
-      "rel": "self",
-      "href": "cb_f1.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": [],
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/cb_f1.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/cb_f1.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": [],
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=cb_f1?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=cb_f1?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=cb_f1?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=cb_f1?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/cb_prophet.json b/catalog/summaries/models/model_items/cb_prophet.json
deleted file mode 100644
index e26e7e4809..0000000000
--- a/catalog/summaries/models/model_items/cb_prophet.json
+++ /dev/null
@@ -1,311 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "cb_prophet",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
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-      [-149.3705, 68.6611],
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-      [-102.4471, 39.7582],
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-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, UNDE, STER, TREE, UKFS, SOAP, STEI, TALL, TOOL, WOOD, SRER, WREF, TEAK, YELL, ARIK, BIGC, BLDE, BLUE, CARI, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU, COMO, CUPE\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily latent_heat_flux, Daily Red_chromatic_coordinate, Daily Dissolved_oxygen, Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily latent_heat_flux",
-      "Daily Red_chromatic_coordinate",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "cb_prophet"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "cb_prophet"
-    },
-    {
-      "rel": "self",
-      "href": "cb_prophet.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/cb_prophet.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/cb_prophet.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=cb_prophet?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/climatology.json b/catalog/summaries/models/model_items/climatology.json
index a78983c976..beb7f88ca8 100644
--- a/catalog/summaries/models/model_items/climatology.json
+++ b/catalog/summaries/models/model_items/climatology.json
@@ -7,96 +7,30 @@
   "id": "climatology",
   "bbox": [
     [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
+      -122.6692,
+      45.5175,
+      -74.7781,
+      45.5175
     ]
   ],
   "geometry": {
     "type": "MultiPoint",
     "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-84.4374, 31.1854],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-122.3303, 45.7624],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-156.6194, 71.2824],
-      [-147.5026, 65.154],
-      [-145.7514, 63.8811],
-      [-149.2133, 63.8758],
-      [-149.3705, 68.6611],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-147.504, 65.1532]
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      []
     ]
   },
   "properties": {
-    "description": ["\nmodel info: Historical DOY mean and sd.  Assumes normal distribution\n\n\nSites: BARC, BLWA, FLNT, SUGG, TOMB, ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BARR, BONA, DEJU, HEAL, TOOL, ARIK, BIGC, BLDE, BLUE, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU, CARI\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, 30min Net_ecosystem_exchange, Daily latent_heat_flux, 30min latent_heat_flux, Daily Red_chromatic_coordinate, Daily Dissolved_oxygen, Daily Water_temperature", "\nmodel info: NA\n\nSites: BARC, BLWA, FLNT, SUGG, TOMB, ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BARR, BONA, DEJU, HEAL, TOOL, ARIK, BIGC, BLDE, BLUE, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU, CARI\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, 30min Net_ecosystem_exchange, Daily latent_heat_flux, 30min latent_heat_flux, Daily Red_chromatic_coordinate, Daily Dissolved_oxygen, Daily Water_temperature"],
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-22",
+    "description": "\nmodel info: Forecasts stream chlorophyll-a based on the historic average and standard deviation for that given site and day-of-year.\n\nSites: USGS-01427510, USGS-01463500, USGS-05543010, USGS-05553700, USGS-05558300, USGS-05586300, USGS-14181500, USGS-14211010, USGS-14211720\n\nVariables: Daily Chlorophyll_a",
+    "start_datetime": "2024-02-07",
+    "end_datetime": "2024-03-14",
     "providers": [
       {
         "url": "pending",
@@ -118,16 +52,8 @@
     "license": "CC0-1.0",
     "keywords": [
       "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "30min Net_ecosystem_exchange",
-      "Daily latent_heat_flux",
-      "30min latent_heat_flux",
-      "Daily Red_chromatic_coordinate",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature"
+      "usgsrc4cast",
+      "Daily Chlorophyll_a"
     ],
     "table:columns": [
       {
@@ -245,7 +171,7 @@
     },
     {
       "rel": "item",
-      "href": ["https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_climatology.R", null],
+      "href": "https://github.com/eco4cast/usgsrc4cast-ci/blob/main/baseline_models/models/aquatics_climatology.R",
       "type": "text/html",
       "title": "Link for Model Code"
     }
@@ -260,62 +186,14 @@
     "2": {
       "type": "text/html",
       "title": "Link for Model Code",
-      "href": ["https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_climatology.R", null],
+      "href": "https://github.com/eco4cast/usgsrc4cast-ci/blob/main/baseline_models/models/aquatics_climatology.R",
       "description": "The link to the model code provided by the model submission team"
     },
     "3": {
       "type": "application/x-parquet",
       "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for 30min Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=PT30M/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=PT30M/variable=nee/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for 30min latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=PT30M/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=PT30M/variable=le/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=climatology?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summariesproject_id=/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org",
+      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/summariesproject_id=/duration=P1D/variable=chla/model_id=climatology?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     }
   }
 }
diff --git a/catalog/summaries/models/model_items/fARIMA.json b/catalog/summaries/models/model_items/fARIMA.json
deleted file mode 100644
index f1236dc927..0000000000
--- a/catalog/summaries/models/model_items/fARIMA.json
+++ /dev/null
@@ -1,224 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "fARIMA",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-10",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fARIMA"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fARIMA"
-    },
-    {
-      "rel": "self",
-      "href": "fARIMA.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fARIMA.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fARIMA.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=fARIMA?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=fARIMA?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/fARIMA_clim_ensemble.json b/catalog/summaries/models/model_items/fARIMA_clim_ensemble.json
deleted file mode 100644
index b3d324f298..0000000000
--- a/catalog/summaries/models/model_items/fARIMA_clim_ensemble.json
+++ /dev/null
@@ -1,218 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "fARIMA_clim_ensemble",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-96.443, 38.9459],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-122.1655, 44.2596],
-      [-83.5038, 35.6904],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-110.5871, 44.9501],
-      [-105.5442, 40.035],
-      [-119.2575, 37.0597],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-96.6038, 39.1051],
-      [-88.1589, 31.8534],
-      [-119.0274, 36.9559]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: MCDI, POSE, PRIN, REDB, SUGG, SYCA, WALK, WLOU, LEWI, MART, MAYF, MCRA, LECO, ARIK, BARC, BLUE, BLWA, CUPE, GUIL, HOPB, BLDE, COMO, BIGC, CRAM, FLNT, KING, TOMB, TECR\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-10",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fARIMA_clim_ensemble"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fARIMA_clim_ensemble"
-    },
-    {
-      "rel": "self",
-      "href": "fARIMA_clim_ensemble.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fARIMA_clim_ensemble.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fARIMA_clim_ensemble.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=fARIMA_clim_ensemble?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=fARIMA_clim_ensemble?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/fTSLM_lag.json b/catalog/summaries/models/model_items/fTSLM_lag.json
deleted file mode 100644
index fd572e1b0a..0000000000
--- a/catalog/summaries/models/model_items/fTSLM_lag.json
+++ /dev/null
@@ -1,224 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "fTSLM_lag",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-10",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fTSLM_lag"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "fTSLM_lag"
-    },
-    {
-      "rel": "self",
-      "href": "fTSLM_lag.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fTSLM_lag.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/fTSLM_lag.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=fTSLM_lag?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=fTSLM_lag?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/flareGLM.json b/catalog/summaries/models/model_items/flareGLM.json
deleted file mode 100644
index 0612168d6a..0000000000
--- a/catalog/summaries/models/model_items/flareGLM.json
+++ /dev/null
@@ -1,197 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flareGLM",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2024-01-16",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGLM"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGLM"
-    },
-    {
-      "rel": "self",
-      "href": "flareGLM.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGLM.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGLM.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=flareGLM?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=flareGLM?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/flareGLM_noDA.json b/catalog/summaries/models/model_items/flareGLM_noDA.json
deleted file mode 100644
index ff4e2b8114..0000000000
--- a/catalog/summaries/models/model_items/flareGLM_noDA.json
+++ /dev/null
@@ -1,197 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flareGLM_noDA",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307],
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: PRPO, SUGG, TOOK, BARC, CRAM, LIRO, PRLA\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2024-01-16",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGLM_noDA"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGLM_noDA"
-    },
-    {
-      "rel": "self",
-      "href": "flareGLM_noDA.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGLM_noDA.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGLM_noDA.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=flareGLM_noDA?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=flareGLM_noDA?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/flareGOTM.json b/catalog/summaries/models/model_items/flareGOTM.json
deleted file mode 100644
index 3bd889a4da..0000000000
--- a/catalog/summaries/models/model_items/flareGOTM.json
+++ /dev/null
@@ -1,196 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flareGOTM",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: PRPO, SUGG, BARC, CRAM, LIRO, PRLA\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2024-01-16",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGOTM"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGOTM"
-    },
-    {
-      "rel": "self",
-      "href": "flareGOTM.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGOTM.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGOTM.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=flareGOTM?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=flareGOTM?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/flareGOTM_noDA.json b/catalog/summaries/models/model_items/flareGOTM_noDA.json
deleted file mode 100644
index 954d0a7712..0000000000
--- a/catalog/summaries/models/model_items/flareGOTM_noDA.json
+++ /dev/null
@@ -1,196 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flareGOTM_noDA",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: PRPO, SUGG, BARC, CRAM, LIRO, PRLA\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2024-01-16",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGOTM_noDA"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareGOTM_noDA"
-    },
-    {
-      "rel": "self",
-      "href": "flareGOTM_noDA.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGOTM_noDA.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareGOTM_noDA.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=flareGOTM_noDA?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=flareGOTM_noDA?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/flareSimstrat.json b/catalog/summaries/models/model_items/flareSimstrat.json
deleted file mode 100644
index b8df68cd5d..0000000000
--- a/catalog/summaries/models/model_items/flareSimstrat.json
+++ /dev/null
@@ -1,196 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flareSimstrat",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-82.0084, 29.676]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: PRPO, SUGG, CRAM, LIRO, PRLA, BARC\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2024-01-16",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareSimstrat"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareSimstrat"
-    },
-    {
-      "rel": "self",
-      "href": "flareSimstrat.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareSimstrat.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareSimstrat.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=flareSimstrat?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=flareSimstrat?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/flareSimstrat_noDA.json b/catalog/summaries/models/model_items/flareSimstrat_noDA.json
deleted file mode 100644
index 66a151a06e..0000000000
--- a/catalog/summaries/models/model_items/flareSimstrat_noDA.json
+++ /dev/null
@@ -1,195 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flareSimstrat_noDA",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, PRLA, PRPO, SUGG\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2024-01-16",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareSimstrat_noDA"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flareSimstrat_noDA"
-    },
-    {
-      "rel": "self",
-      "href": "flareSimstrat_noDA.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareSimstrat_noDA.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flareSimstrat_noDA.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=flareSimstrat_noDA?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=flareSimstrat_noDA?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/flare_ler.json b/catalog/summaries/models/model_items/flare_ler.json
deleted file mode 100644
index 3aee1887eb..0000000000
--- a/catalog/summaries/models/model_items/flare_ler.json
+++ /dev/null
@@ -1,196 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flare_ler",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flare_ler"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flare_ler"
-    },
-    {
-      "rel": "self",
-      "href": "flare_ler.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flare_ler.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flare_ler.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=flare_ler?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=flare_ler?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/flare_ler_baselines.json b/catalog/summaries/models/model_items/flare_ler_baselines.json
deleted file mode 100644
index 7e50f07f7e..0000000000
--- a/catalog/summaries/models/model_items/flare_ler_baselines.json
+++ /dev/null
@@ -1,192 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "flare_ler_baselines",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-82.0177, 29.6878]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, SUGG\n\nVariables: Daily Water_temperature",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flare_ler_baselines"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "flare_ler_baselines"
-    },
-    {
-      "rel": "self",
-      "href": "flare_ler_baselines.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flare_ler_baselines.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/flare_ler_baselines.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=flare_ler_baselines?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=flare_ler_baselines?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/lasso.json b/catalog/summaries/models/model_items/lasso.json
deleted file mode 100644
index a7874ff4ea..0000000000
--- a/catalog/summaries/models/model_items/lasso.json
+++ /dev/null
@@ -1,244 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "lasso",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Net_ecosystem_exchange",
-      "Daily latent_heat_flux"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "lasso"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "lasso"
-    },
-    {
-      "rel": "self",
-      "href": "lasso.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": [],
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/lasso.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/lasso.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": [],
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/mean.json b/catalog/summaries/models/model_items/mean.json
deleted file mode 100644
index ebd982ed14..0000000000
--- a/catalog/summaries/models/model_items/mean.json
+++ /dev/null
@@ -1,244 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "mean",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-78.0418, 39.0337],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-81.9934, 29.6893],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-80.5248, 37.3783],
-      [-100.9154, 46.7697],
-      [-122.3303, 45.7624],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-121.9519, 45.8205],
-      [-145.7514, 63.8811],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-87.8039, 32.5417],
-      [-78.1395, 38.8929],
-      [-110.5391, 44.9535],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-72.1727, 42.5369],
-      [-99.0588, 35.4106]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-20",
-    "end_datetime": "2024-12-09",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Weekly beetle_community_richness",
-      "Weekly beetle_community_abundance"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "mean"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "mean"
-    },
-    {
-      "rel": "self",
-      "href": "mean.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": [],
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/mean.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/mean.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": [],
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=mean?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=mean?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=mean?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=mean?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/null.json b/catalog/summaries/models/model_items/null.json
deleted file mode 100644
index b621f9fc09..0000000000
--- a/catalog/summaries/models/model_items/null.json
+++ /dev/null
@@ -1,231 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "null",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-10-10",
-    "end_datetime": "2024-10-08",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "null"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "null"
-    },
-    {
-      "rel": "self",
-      "href": "null.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": [],
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/null.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/null.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": [],
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=null?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=null?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=null?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=null?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/persistenceRW.json b/catalog/summaries/models/model_items/persistenceRW.json
index 2cdb782767..be7a7ce474 100644
--- a/catalog/summaries/models/model_items/persistenceRW.json
+++ b/catalog/summaries/models/model_items/persistenceRW.json
@@ -7,102 +7,31 @@
   "id": "persistenceRW",
   "bbox": [
     [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
+      -122.6692,
+      45.5175,
+      -74.7781,
+      45.5175
     ]
   ],
   "geometry": {
     "type": "MultiPoint",
     "coordinates": [
-      [-149.6106, 68.6307],
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-111.5081, 33.751],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-96.6038, 39.1051],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719]
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      [],
+      []
     ]
   },
   "properties": {
-    "description": ["\nmodel info: Random walk from the fable package with ensembles used to represent uncertainty\n\nSites: TOOK, BARC, BLWA, CRAM, FLNT, LIRO, SJER, SOAP, SRER, STEI, STER, TALL, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, HEAL, JERC, JORN, KONA, KONZ, OSBS, PUUM, RMNP, SCBI, SERC, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, OAES, ONAQ, ORNL, UNDE, WOOD, WREF, YELL, TEAK, TOOL, TREE, UKFS, DSNY, GRSM, GUAN, HARV, TECR, TOMB, WALK, WLOU, SYCA, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, LECO, LEWI, MART, PRLA, PRPO, REDB, SUGG, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, KING, GUIL, HOPB\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Red_chromatic_coordinate, Daily Dissolved_oxygen, Daily Water_temperature", "\nmodel info: NA\n\nSites: TOOK, BARC, BLWA, CRAM, FLNT, LIRO, SJER, SOAP, SRER, STEI, STER, TALL, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, HEAL, JERC, JORN, KONA, KONZ, OSBS, PUUM, RMNP, SCBI, SERC, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, OAES, ONAQ, ORNL, UNDE, WOOD, WREF, YELL, TEAK, TOOL, TREE, UKFS, DSNY, GRSM, GUAN, HARV, TECR, TOMB, WALK, WLOU, SYCA, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, LECO, LEWI, MART, PRLA, PRPO, REDB, SUGG, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, KING, GUIL, HOPB\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Red_chromatic_coordinate, Daily Dissolved_oxygen, Daily Water_temperature"],
-    "start_datetime": "2023-11-15",
-    "end_datetime": "2024-01-20",
+    "description": "\nmodel info: Random walk model based on most recent stream chl-a observations using the fable::RW() model.\n\nSites: USGS-01427510, USGS-01463500, USGS-05543010, USGS-05549500, USGS-05553700, USGS-05558300, USGS-05586300, USGS-14181500, USGS-14211010, USGS-14211720\n\nVariables: Daily Chlorophyll_a",
+    "start_datetime": "2024-02-07",
+    "end_datetime": "2024-03-13",
     "providers": [
       {
         "url": "pending",
@@ -124,13 +53,8 @@
     "license": "CC0-1.0",
     "keywords": [
       "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily Red_chromatic_coordinate",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature"
+      "usgsrc4cast",
+      "Daily Chlorophyll_a"
     ],
     "table:columns": [
       {
@@ -248,7 +172,7 @@
     },
     {
       "rel": "item",
-      "href": ["https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R", null],
+      "href": "https://github.com/eco4cast/usgsrc4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R",
       "type": "text/html",
       "title": "Link for Model Code"
     }
@@ -263,44 +187,14 @@
     "2": {
       "type": "text/html",
       "title": "Link for Model Code",
-      "href": ["https://github.com/eco4cast/neon4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R", null],
+      "href": "https://github.com/eco4cast/usgsrc4cast-ci/blob/main/baseline_models/models/aquatics_persistenceRW.R",
       "description": "The link to the model code provided by the model submission team"
     },
     "3": {
       "type": "application/x-parquet",
       "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
+      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summariesproject_id=/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org",
+      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230014-bucket01/challenges/forecasts/summariesproject_id=/duration=P1D/variable=chla/model_id=persistenceRW?endpoint_override=sdsc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
     }
   }
 }
diff --git a/catalog/summaries/models/model_items/procBlanchardMonod.json b/catalog/summaries/models/model_items/procBlanchardMonod.json
deleted file mode 100644
index 239e53fb01..0000000000
--- a/catalog/summaries/models/model_items/procBlanchardMonod.json
+++ /dev/null
@@ -1,197 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procBlanchardMonod",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procBlanchardMonod"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procBlanchardMonod"
-    },
-    {
-      "rel": "self",
-      "href": "procBlanchardMonod.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procBlanchardMonod.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procBlanchardMonod.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=procBlanchardMonod?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=procBlanchardMonod?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/procBlanchardSteele.json b/catalog/summaries/models/model_items/procBlanchardSteele.json
deleted file mode 100644
index 3b9587ea91..0000000000
--- a/catalog/summaries/models/model_items/procBlanchardSteele.json
+++ /dev/null
@@ -1,197 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procBlanchardSteele",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procBlanchardSteele"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procBlanchardSteele"
-    },
-    {
-      "rel": "self",
-      "href": "procBlanchardSteele.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": [],
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procBlanchardSteele.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procBlanchardSteele.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": [],
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=procBlanchardSteele?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=procBlanchardSteele?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/procCTMIMonod.json b/catalog/summaries/models/model_items/procCTMIMonod.json
deleted file mode 100644
index b2574d9dbd..0000000000
--- a/catalog/summaries/models/model_items/procCTMIMonod.json
+++ /dev/null
@@ -1,197 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procCTMIMonod",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procCTMIMonod"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procCTMIMonod"
-    },
-    {
-      "rel": "self",
-      "href": "procCTMIMonod.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procCTMIMonod.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procCTMIMonod.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=procCTMIMonod?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=procCTMIMonod?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/procCTMISteele.json b/catalog/summaries/models/model_items/procCTMISteele.json
deleted file mode 100644
index 9f3db2ba9f..0000000000
--- a/catalog/summaries/models/model_items/procCTMISteele.json
+++ /dev/null
@@ -1,197 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procCTMISteele",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procCTMISteele"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procCTMISteele"
-    },
-    {
-      "rel": "self",
-      "href": "procCTMISteele.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": [],
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procCTMISteele.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procCTMISteele.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": [],
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=procCTMISteele?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=procCTMISteele?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/procEppleyNorbergMonod.json b/catalog/summaries/models/model_items/procEppleyNorbergMonod.json
deleted file mode 100644
index 7720c4a437..0000000000
--- a/catalog/summaries/models/model_items/procEppleyNorbergMonod.json
+++ /dev/null
@@ -1,197 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procEppleyNorbergMonod",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procEppleyNorbergMonod"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procEppleyNorbergMonod"
-    },
-    {
-      "rel": "self",
-      "href": "procEppleyNorbergMonod.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procEppleyNorbergMonod.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procEppleyNorbergMonod.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=procEppleyNorbergMonod?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=procEppleyNorbergMonod?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/procEppleyNorbergSteele.json b/catalog/summaries/models/model_items/procEppleyNorbergSteele.json
deleted file mode 100644
index 36e0aa7e47..0000000000
--- a/catalog/summaries/models/model_items/procEppleyNorbergSteele.json
+++ /dev/null
@@ -1,197 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procEppleyNorbergSteele",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procEppleyNorbergSteele"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procEppleyNorbergSteele"
-    },
-    {
-      "rel": "self",
-      "href": "procEppleyNorbergSteele.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procEppleyNorbergSteele.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procEppleyNorbergSteele.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=procEppleyNorbergSteele?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=procEppleyNorbergSteele?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/procHinshelwoodMonod.json b/catalog/summaries/models/model_items/procHinshelwoodMonod.json
deleted file mode 100644
index 3ad54ebe2b..0000000000
--- a/catalog/summaries/models/model_items/procHinshelwoodMonod.json
+++ /dev/null
@@ -1,197 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procHinshelwoodMonod",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procHinshelwoodMonod"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procHinshelwoodMonod"
-    },
-    {
-      "rel": "self",
-      "href": "procHinshelwoodMonod.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procHinshelwoodMonod.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procHinshelwoodMonod.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=procHinshelwoodMonod?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=procHinshelwoodMonod?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/procHinshelwoodSteele.json b/catalog/summaries/models/model_items/procHinshelwoodSteele.json
deleted file mode 100644
index 1b5fb4ba2b..0000000000
--- a/catalog/summaries/models/model_items/procHinshelwoodSteele.json
+++ /dev/null
@@ -1,197 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "procHinshelwoodSteele",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-89.4737, 46.2097],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-149.6106, 68.6307]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK\n\nVariables: Daily Chlorophyll_a",
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2024-01-15",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procHinshelwoodSteele"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "procHinshelwoodSteele"
-    },
-    {
-      "rel": "self",
-      "href": "procHinshelwoodSteele.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procHinshelwoodSteele.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/procHinshelwoodSteele.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=procHinshelwoodSteele?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=procHinshelwoodSteele?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/prophet_clim_ensemble.json b/catalog/summaries/models/model_items/prophet_clim_ensemble.json
deleted file mode 100644
index 0f37d3d3ff..0000000000
--- a/catalog/summaries/models/model_items/prophet_clim_ensemble.json
+++ /dev/null
@@ -1,232 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "prophet_clim_ensemble",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-119.006, 37.0058],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-122.3303, 45.7624],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-13",
-    "end_datetime": "2024-01-14",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Red_chromatic_coordinate"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "prophet_clim_ensemble"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "prophet_clim_ensemble"
-    },
-    {
-      "rel": "self",
-      "href": "prophet_clim_ensemble.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": [],
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/prophet_clim_ensemble.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/prophet_clim_ensemble.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": [],
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=prophet_clim_ensemble?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=prophet_clim_ensemble?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/randfor.json b/catalog/summaries/models/model_items/randfor.json
deleted file mode 100644
index 594a96f67f..0000000000
--- a/catalog/summaries/models/model_items/randfor.json
+++ /dev/null
@@ -1,237 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "randfor",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily latent_heat_flux"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "randfor"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "randfor"
-    },
-    {
-      "rel": "self",
-      "href": "randfor.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": [],
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/randfor.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/randfor.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": [],
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/tg_arima.json b/catalog/summaries/models/model_items/tg_arima.json
deleted file mode 100644
index 277b58fc2b..0000000000
--- a/catalog/summaries/models/model_items/tg_arima.json
+++ /dev/null
@@ -1,334 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_arima",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily latent_heat_flux, Daily Red_chromatic_coordinate, Daily Dissolved_oxygen, Daily Water_temperature, Weekly beetle_community_richness, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population",
-    "start_datetime": "2023-01-01",
-    "end_datetime": "2024-12-09",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily latent_heat_flux",
-      "Daily Red_chromatic_coordinate",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature",
-      "Weekly beetle_community_richness",
-      "Weekly beetle_community_abundance",
-      "Weekly Amblyomma_americanum_population"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_arima"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_arima"
-    },
-    {
-      "rel": "self",
-      "href": "tg_arima.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_arima.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_arima.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_arima?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/tg_auto_adam.json b/catalog/summaries/models/model_items/tg_auto_adam.json
deleted file mode 100644
index 8779503df1..0000000000
--- a/catalog/summaries/models/model_items/tg_auto_adam.json
+++ /dev/null
@@ -1,334 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_auto_adam",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-01-01",
-    "end_datetime": "2023-12-26",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily latent_heat_flux",
-      "Weekly beetle_community_abundance",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Weekly beetle_community_richness",
-      "Daily Chlorophyll_a",
-      "Weekly Amblyomma_americanum_population",
-      "Daily Water_temperature"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_auto_adam"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_auto_adam"
-    },
-    {
-      "rel": "self",
-      "href": "tg_auto_adam.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": [],
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_auto_adam.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_auto_adam.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": [],
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_auto_adam?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/tg_bag_mlp.json b/catalog/summaries/models/model_items/tg_bag_mlp.json
deleted file mode 100644
index 58a7a94c50..0000000000
--- a/catalog/summaries/models/model_items/tg_bag_mlp.json
+++ /dev/null
@@ -1,313 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_bag_mlp",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily latent_heat_flux",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature",
-      "Daily Dissolved_oxygen"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_bag_mlp"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_bag_mlp"
-    },
-    {
-      "rel": "self",
-      "href": "tg_bag_mlp.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": [],
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_bag_mlp.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_bag_mlp.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": [],
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_bag_mlp?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/tg_ets.json b/catalog/summaries/models/model_items/tg_ets.json
deleted file mode 100644
index 9bd16aab3a..0000000000
--- a/catalog/summaries/models/model_items/tg_ets.json
+++ /dev/null
@@ -1,334 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_ets",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily latent_heat_flux, Daily Dissolved_oxygen, Daily Red_chromatic_coordinate, Daily Water_temperature, Weekly beetle_community_richness, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population",
-    "start_datetime": "2023-01-01",
-    "end_datetime": "2024-12-09",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily latent_heat_flux",
-      "Daily Dissolved_oxygen",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature",
-      "Weekly beetle_community_richness",
-      "Weekly beetle_community_abundance",
-      "Weekly Amblyomma_americanum_population"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_ets"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_ets"
-    },
-    {
-      "rel": "self",
-      "href": "tg_ets.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_ets.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_ets.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_ets?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/tg_humidity_lm.json b/catalog/summaries/models/model_items/tg_humidity_lm.json
deleted file mode 100644
index c53ae1fa36..0000000000
--- a/catalog/summaries/models/model_items/tg_humidity_lm.json
+++ /dev/null
@@ -1,334 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_humidity_lm",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SYCA, TECR, WALK, WLOU, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Daily Water_temperature, Weekly beetle_community_abundance, Daily Red_chromatic_coordinate, Daily latent_heat_flux, Weekly Amblyomma_americanum_population, Weekly beetle_community_richness",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-20",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature",
-      "Weekly beetle_community_abundance",
-      "Daily Red_chromatic_coordinate",
-      "Daily latent_heat_flux",
-      "Weekly Amblyomma_americanum_population",
-      "Weekly beetle_community_richness"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_humidity_lm"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_humidity_lm"
-    },
-    {
-      "rel": "self",
-      "href": "tg_humidity_lm.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_humidity_lm.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_humidity_lm.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_humidity_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/tg_humidity_lm_all_sites.json b/catalog/summaries/models/model_items/tg_humidity_lm_all_sites.json
deleted file mode 100644
index 1c3bc6ba83..0000000000
--- a/catalog/summaries/models/model_items/tg_humidity_lm_all_sites.json
+++ /dev/null
@@ -1,334 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_humidity_lm_all_sites",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR\n\nVariables: Daily Green_chromatic_coordinate, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Dissolved_oxygen, Weekly Amblyomma_americanum_population, Daily Water_temperature, Daily Red_chromatic_coordinate, Daily Chlorophyll_a, Weekly beetle_community_richness, Weekly beetle_community_abundance",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-20",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Green_chromatic_coordinate",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "Daily Dissolved_oxygen",
-      "Weekly Amblyomma_americanum_population",
-      "Daily Water_temperature",
-      "Daily Red_chromatic_coordinate",
-      "Daily Chlorophyll_a",
-      "Weekly beetle_community_richness",
-      "Weekly beetle_community_abundance"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_humidity_lm_all_sites"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_humidity_lm_all_sites"
-    },
-    {
-      "rel": "self",
-      "href": "tg_humidity_lm_all_sites.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_humidity_lm_all_sites.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_humidity_lm_all_sites.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_humidity_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/tg_lasso.json b/catalog/summaries/models/model_items/tg_lasso.json
deleted file mode 100644
index d8853f5fca..0000000000
--- a/catalog/summaries/models/model_items/tg_lasso.json
+++ /dev/null
@@ -1,320 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_lasso",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Dissolved_oxygen, Weekly beetle_community_abundance, Daily Water_temperature, Daily Red_chromatic_coordinate, Weekly beetle_community_richness, Weekly Amblyomma_americanum_population",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-20",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Dissolved_oxygen",
-      "Weekly beetle_community_abundance",
-      "Daily Water_temperature",
-      "Daily Red_chromatic_coordinate",
-      "Weekly beetle_community_richness",
-      "Weekly Amblyomma_americanum_population"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_lasso"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_lasso"
-    },
-    {
-      "rel": "self",
-      "href": "tg_lasso.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_lasso.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_lasso.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_lasso?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/tg_lasso_all_sites.json b/catalog/summaries/models/model_items/tg_lasso_all_sites.json
deleted file mode 100644
index a9ad7ef134..0000000000
--- a/catalog/summaries/models/model_items/tg_lasso_all_sites.json
+++ /dev/null
@@ -1,320 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_lasso_all_sites",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature",
-      "Daily Red_chromatic_coordinate",
-      "Weekly beetle_community_abundance",
-      "Weekly Amblyomma_americanum_population",
-      "Weekly beetle_community_richness"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_lasso_all_sites"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_lasso_all_sites"
-    },
-    {
-      "rel": "self",
-      "href": "tg_lasso_all_sites.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": [],
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_lasso_all_sites.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_lasso_all_sites.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": [],
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_lasso_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/tg_precip_lm.json b/catalog/summaries/models/model_items/tg_precip_lm.json
deleted file mode 100644
index c822847dab..0000000000
--- a/catalog/summaries/models/model_items/tg_precip_lm.json
+++ /dev/null
@@ -1,334 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_precip_lm",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
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-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, SYCA, TECR, WALK, WLOU, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB\n\nVariables: Daily Chlorophyll_a, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily latent_heat_flux, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population, Daily Dissolved_oxygen, Daily Water_temperature, Daily Red_chromatic_coordinate, Weekly beetle_community_richness",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-20",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily latent_heat_flux",
-      "Weekly beetle_community_abundance",
-      "Weekly Amblyomma_americanum_population",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature",
-      "Daily Red_chromatic_coordinate",
-      "Weekly beetle_community_richness"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_precip_lm"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_precip_lm"
-    },
-    {
-      "rel": "self",
-      "href": "tg_precip_lm.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_precip_lm.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_precip_lm.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_precip_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/tg_precip_lm_all_sites.json b/catalog/summaries/models/model_items/tg_precip_lm_all_sites.json
deleted file mode 100644
index 6ed2c42417..0000000000
--- a/catalog/summaries/models/model_items/tg_precip_lm_all_sites.json
+++ /dev/null
@@ -1,334 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_precip_lm_all_sites",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, WALK, WLOU, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR\n\nVariables: Daily Chlorophyll_a, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Green_chromatic_coordinate, Weekly beetle_community_richness, Daily Dissolved_oxygen, Daily Water_temperature, Daily Red_chromatic_coordinate, Weekly Amblyomma_americanum_population, Weekly beetle_community_abundance",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-20",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "Daily Green_chromatic_coordinate",
-      "Weekly beetle_community_richness",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature",
-      "Daily Red_chromatic_coordinate",
-      "Weekly Amblyomma_americanum_population",
-      "Weekly beetle_community_abundance"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_precip_lm_all_sites"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_precip_lm_all_sites"
-    },
-    {
-      "rel": "self",
-      "href": "tg_precip_lm_all_sites.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_precip_lm_all_sites.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_precip_lm_all_sites.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_precip_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/tg_randfor.json b/catalog/summaries/models/model_items/tg_randfor.json
deleted file mode 100644
index 239dffdc43..0000000000
--- a/catalog/summaries/models/model_items/tg_randfor.json
+++ /dev/null
@@ -1,334 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_randfor",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU\n\nVariables: Daily Chlorophyll_a, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Green_chromatic_coordinate, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population, Daily Dissolved_oxygen, Daily Water_temperature, Daily Red_chromatic_coordinate, Weekly beetle_community_richness",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "Daily Green_chromatic_coordinate",
-      "Weekly beetle_community_abundance",
-      "Weekly Amblyomma_americanum_population",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature",
-      "Daily Red_chromatic_coordinate",
-      "Weekly beetle_community_richness"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_randfor"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_randfor"
-    },
-    {
-      "rel": "self",
-      "href": "tg_randfor.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_randfor.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_randfor.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_randfor?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/tg_randfor_all_sites.json b/catalog/summaries/models/model_items/tg_randfor_all_sites.json
deleted file mode 100644
index 574958e44a..0000000000
--- a/catalog/summaries/models/model_items/tg_randfor_all_sites.json
+++ /dev/null
@@ -1,306 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_randfor_all_sites",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035]
-    ]
-  },
-  "properties": {
-    "description": [],
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-19",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Weekly beetle_community_richness",
-      "Daily Dissolved_oxygen",
-      "Daily Water_temperature",
-      "Daily Red_chromatic_coordinate",
-      "Weekly Amblyomma_americanum_population",
-      "Weekly beetle_community_abundance"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_randfor_all_sites"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_randfor_all_sites"
-    },
-    {
-      "rel": "self",
-      "href": "tg_randfor_all_sites.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": [],
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_randfor_all_sites.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_randfor_all_sites.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": [],
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_randfor_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/tg_tbats.json b/catalog/summaries/models/model_items/tg_tbats.json
deleted file mode 100644
index b70b8cd056..0000000000
--- a/catalog/summaries/models/model_items/tg_tbats.json
+++ /dev/null
@@ -1,334 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_tbats",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU, ARIK, BIGC, BLDE, BLUE\n\nVariables: Daily Chlorophyll_a, Daily latent_heat_flux, Daily Net_ecosystem_exchange, Daily Red_chromatic_coordinate, Daily Green_chromatic_coordinate, Weekly Amblyomma_americanum_population, Weekly beetle_community_richness, Daily Water_temperature, Weekly beetle_community_abundance, Daily Dissolved_oxygen",
-    "start_datetime": "2023-01-01",
-    "end_datetime": "2024-12-09",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily latent_heat_flux",
-      "Daily Net_ecosystem_exchange",
-      "Daily Red_chromatic_coordinate",
-      "Daily Green_chromatic_coordinate",
-      "Weekly Amblyomma_americanum_population",
-      "Weekly beetle_community_richness",
-      "Daily Water_temperature",
-      "Weekly beetle_community_abundance",
-      "Daily Dissolved_oxygen"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_tbats"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_tbats"
-    },
-    {
-      "rel": "self",
-      "href": "tg_tbats.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_tbats.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_tbats.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_tbats?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/tg_temp_lm.json b/catalog/summaries/models/model_items/tg_temp_lm.json
deleted file mode 100644
index 0e283bbda7..0000000000
--- a/catalog/summaries/models/model_items/tg_temp_lm.json
+++ /dev/null
@@ -1,334 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_temp_lm",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-149.6106, 68.6307],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-82.0084, 29.676],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-87.7982, 32.5415],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-89.4737, 46.2097],
-      [-66.9868, 18.1135],
-      [-84.4374, 31.1854],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-89.7048, 45.9983],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-111.7979, 40.7839],
-      [-82.0177, 29.6878],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-88.1589, 31.8534]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB\n\nVariables: Daily latent_heat_flux, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Red_chromatic_coordinate, Daily Water_temperature, Daily Dissolved_oxygen, Daily Chlorophyll_a, Weekly beetle_community_abundance, Weekly Amblyomma_americanum_population, Weekly beetle_community_richness",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-20",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily latent_heat_flux",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature",
-      "Daily Dissolved_oxygen",
-      "Daily Chlorophyll_a",
-      "Weekly beetle_community_abundance",
-      "Weekly Amblyomma_americanum_population",
-      "Weekly beetle_community_richness"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_temp_lm"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_temp_lm"
-    },
-    {
-      "rel": "self",
-      "href": "tg_temp_lm.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_temp_lm.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_temp_lm.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_temp_lm?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/models/model_items/tg_temp_lm_all_sites.json b/catalog/summaries/models/model_items/tg_temp_lm_all_sites.json
deleted file mode 100644
index ff65c4d293..0000000000
--- a/catalog/summaries/models/model_items/tg_temp_lm_all_sites.json
+++ /dev/null
@@ -1,334 +0,0 @@
-{
-  "stac_version": "1.0.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/table/v1.2.0/schema.json"
-  ],
-  "type": "Feature",
-  "id": "tg_temp_lm_all_sites",
-  "bbox": [
-    [
-      -156.6194,
-      71.2824,
-      -66.7987,
-      71.2824
-    ]
-  ],
-  "geometry": {
-    "type": "MultiPoint",
-    "coordinates": [
-      [-82.0084, 29.676],
-      [-87.7982, 32.5415],
-      [-89.4737, 46.2097],
-      [-84.4374, 31.1854],
-      [-89.7048, 45.9983],
-      [-99.1139, 47.1591],
-      [-99.2531, 47.1298],
-      [-82.0177, 29.6878],
-      [-88.1589, 31.8534],
-      [-149.6106, 68.6307],
-      [-122.3303, 45.7624],
-      [-156.6194, 71.2824],
-      [-71.2874, 44.0639],
-      [-78.0418, 39.0337],
-      [-147.5026, 65.154],
-      [-97.57, 33.4012],
-      [-104.7456, 40.8155],
-      [-99.1066, 47.1617],
-      [-145.7514, 63.8811],
-      [-87.8039, 32.5417],
-      [-81.4362, 28.1251],
-      [-83.5019, 35.689],
-      [-66.8687, 17.9696],
-      [-72.1727, 42.5369],
-      [-149.2133, 63.8758],
-      [-84.4686, 31.1948],
-      [-106.8425, 32.5907],
-      [-96.6129, 39.1104],
-      [-96.5631, 39.1008],
-      [-67.0769, 18.0213],
-      [-88.1612, 31.8539],
-      [-80.5248, 37.3783],
-      [-109.3883, 38.2483],
-      [-105.5824, 40.0543],
-      [-100.9154, 46.7697],
-      [-99.0588, 35.4106],
-      [-112.4524, 40.1776],
-      [-84.2826, 35.9641],
-      [-81.9934, 29.6893],
-      [-155.3173, 19.5531],
-      [-105.546, 40.2759],
-      [-78.1395, 38.8929],
-      [-76.56, 38.8901],
-      [-119.7323, 37.1088],
-      [-119.2622, 37.0334],
-      [-110.8355, 31.9107],
-      [-89.5864, 45.5089],
-      [-103.0293, 40.4619],
-      [-87.3933, 32.9505],
-      [-119.006, 37.0058],
-      [-149.3705, 68.6611],
-      [-89.5857, 45.4937],
-      [-95.1921, 39.0404],
-      [-89.5373, 46.2339],
-      [-99.2413, 47.1282],
-      [-121.9519, 45.8205],
-      [-110.5391, 44.9535],
-      [-122.1655, 44.2596],
-      [-149.143, 68.6698],
-      [-78.1473, 38.8943],
-      [-97.7823, 33.3785],
-      [-111.7979, 40.7839],
-      [-111.5081, 33.751],
-      [-119.0274, 36.9559],
-      [-84.2793, 35.9574],
-      [-105.9154, 39.8914],
-      [-102.4471, 39.7582],
-      [-119.2575, 37.0597],
-      [-110.5871, 44.9501],
-      [-96.6242, 34.4442],
-      [-147.504, 65.1532],
-      [-105.5442, 40.035],
-      [-66.9868, 18.1135],
-      [-66.7987, 18.1741],
-      [-72.3295, 42.4719],
-      [-96.6038, 39.1051],
-      [-83.5038, 35.6904],
-      [-77.9832, 39.0956],
-      [-121.9338, 45.7908],
-      [-87.4077, 32.9604],
-      [-96.443, 38.9459]
-    ]
-  },
-  "properties": {
-    "description": "\nmodel info: NA\n\nSites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, MCRA, OKSR, POSE, PRIN, REDB, SYCA, TECR, WALK, WLOU, ARIK, BIGC, BLDE, BLUE, CARI, COMO, CUPE, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI\n\nVariables: Daily Chlorophyll_a, Daily latent_heat_flux, Daily Green_chromatic_coordinate, Daily Net_ecosystem_exchange, Daily Red_chromatic_coordinate, Daily Water_temperature, Daily Dissolved_oxygen, Weekly Amblyomma_americanum_population, Weekly beetle_community_richness, Weekly beetle_community_abundance",
-    "start_datetime": "2023-11-14",
-    "end_datetime": "2024-01-20",
-    "providers": [
-      {
-        "url": "pending",
-        "name": "pending",
-        "roles": [
-          "producer",
-          "processor",
-          "licensor"
-        ]
-      },
-      {
-        "url": "https://www.ecoforecastprojectvt.org",
-        "name": "Ecoforecast Challenge",
-        "roles": [
-          "host"
-        ]
-      }
-    ],
-    "license": "CC0-1.0",
-    "keywords": [
-      "Forecasting",
-      "neon4cast",
-      "Daily Chlorophyll_a",
-      "Daily latent_heat_flux",
-      "Daily Green_chromatic_coordinate",
-      "Daily Net_ecosystem_exchange",
-      "Daily Red_chromatic_coordinate",
-      "Daily Water_temperature",
-      "Daily Dissolved_oxygen",
-      "Weekly Amblyomma_americanum_population",
-      "Weekly beetle_community_richness",
-      "Weekly beetle_community_abundance"
-    ],
-    "table:columns": [
-      {
-        "name": "reference_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that the forecast was initiated (horizon = 0)"
-      },
-      {
-        "name": "site_id",
-        "type": "string",
-        "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat)"
-      },
-      {
-        "name": "datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime of the forecasted value (ISO 8601)"
-      },
-      {
-        "name": "family",
-        "type": "string",
-        "description": "For ensembles: “ensemble.” Default value if unspecified for probability distributions: Name of the statistical distribution associated with the reported statistics. The “sample” distribution is synonymous with “ensemble.”For summary statistics: “summary.”"
-      },
-      {
-        "name": "pub_datetime",
-        "type": "timestamp[us, tz=UTC]",
-        "description": "datetime that forecast was submitted"
-      },
-      {
-        "name": "mean",
-        "type": "double",
-        "description": "mean forecast prediction"
-      },
-      {
-        "name": "median",
-        "type": "double",
-        "description": "median forecast prediction"
-      },
-      {
-        "name": "sd",
-        "type": "double",
-        "description": "standard deviation forecasts"
-      },
-      {
-        "name": "quantile97.5",
-        "type": "double",
-        "description": "upper 97.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile02.5",
-        "type": "double",
-        "description": "upper 2.5 percentile value of forecast"
-      },
-      {
-        "name": "quantile90",
-        "type": "double",
-        "description": "upper 90 percentile value of forecast"
-      },
-      {
-        "name": "quantile10",
-        "type": "double",
-        "description": "upper 10 percentile value of forecast"
-      },
-      {
-        "name": "project_id",
-        "type": "string",
-        "description": "unique identifier for the forecast project"
-      },
-      {
-        "name": "duration",
-        "type": "string",
-        "description": "temporal duration of forecast (hourly, daily, etc.); follows ISO 8601 duration convention"
-      },
-      {
-        "name": "variable",
-        "type": "string",
-        "description": "name of forecasted variable"
-      },
-      {
-        "name": "model_id",
-        "type": "string",
-        "description": "unique model identifier"
-      },
-      {
-        "name": "reference_date",
-        "type": "string",
-        "description": "date that the forecast was initiated"
-      }
-    ]
-  },
-  "collection": "forecasts",
-  "links": [
-    {
-      "rel": "collection",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_temp_lm_all_sites"
-    },
-    {
-      "rel": "root",
-      "href": "../../../catalog.json",
-      "type": "application/json",
-      "title": "Forecast Catalog"
-    },
-    {
-      "rel": "parent",
-      "href": "../collection.json",
-      "type": "application/json",
-      "title": "tg_temp_lm_all_sites"
-    },
-    {
-      "rel": "self",
-      "href": "tg_temp_lm_all_sites.json",
-      "type": "application/json",
-      "title": "Model Forecast"
-    },
-    {
-      "rel": "item",
-      "href": null,
-      "type": "text/html",
-      "title": "Link for Model Code"
-    }
-  ],
-  "assets": {
-    "1": {
-      "type": "application/json",
-      "title": "Model Metadata",
-      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_temp_lm_all_sites.json",
-      "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n      \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/metadata/model_id/tg_temp_lm_all_sites.json\")\n\n"
-    },
-    "2": {
-      "type": "text/html",
-      "title": "Link for Model Code",
-      "href": null,
-      "description": "The link to the model code provided by the model submission team"
-    },
-    "3": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Chlorophyll_a",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=chla/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "4": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily latent_heat_flux",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=le/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "5": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Green_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=gcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "6": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Net_ecosystem_exchange",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=nee/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "7": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Red_chromatic_coordinate",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=rcc_90/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "8": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Water_temperature",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=temperature/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "9": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Daily Dissolved_oxygen",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1D/variable=oxygen/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "10": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly Amblyomma_americanum_population",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=amblyomma_americanum/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "11": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_richness",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=richness/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    },
-    "12": {
-      "type": "application/x-parquet",
-      "title": "Database Access for Weekly beetle_community_abundance",
-      "href": "s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org",
-      "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(s3://anonymous@bio230014-bucket01/challenges/forecasts/summaries/parquet/duration=P1W/variable=abundance/model_id=tg_temp_lm_all_sites?endpoint_override=sdsc.osn.xsede.org)\ndf <- all_results |> dplyr::collect()\n\n```\n       \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n"
-    }
-  }
-}
diff --git a/catalog/summaries/summaries_models.R b/catalog/summaries/summaries_models.R
index 364b8cf116..d790cdbb4d 100644
--- a/catalog/summaries/summaries_models.R
+++ b/catalog/summaries/summaries_models.R
@@ -41,7 +41,8 @@ summaries_theme_df <- arrow::open_dataset(arrow::s3_bucket(config$summaries_buck
 
 summaries_data_df <- duckdbfs::open_dataset(glue::glue("s3://{config$inventory_bucket}/catalog/forecasts"),
                                             s3_endpoint = config$endpoint, anonymous=TRUE) |>
-  collect()
+  collect() |>
+  dplyr::filter(project_id == config$project_id)
 
 theme_models <- summaries_data_df |>
   distinct(model_id)
diff --git a/catalog/targets/collection.json b/catalog/targets/collection.json
index 73c05d6cd4..4ae1766854 100644
--- a/catalog/targets/collection.json
+++ b/catalog/targets/collection.json
@@ -31,14 +31,14 @@
     },
     {
       "rel": "about",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
       "type": "text/html",
-      "title": "NEON Ecological Forecasting Challenge Documentation"
+      "title": "EFI-USGS River Chlorophyll Forecasting Challenge Documentation"
     },
     {
       "rel": "describedby",
-      "href": "https://projects.ecoforecast.org/neon4cast-docs/",
-      "title": "NEON Forecast Challenge Dashboard",
+      "href": "https://projects.ecoforecast.org/usgsrc4cast-docs/",
+      "title": "EFI-USGS River Chlorophyll Forecast Challenge Dashboard",
       "type": "text/html"
     }
   ],
@@ -47,18 +47,18 @@
     "spatial": {
       "bbox": [
         [
-          -156.6194,
-          17.9696,
-          -66.7987,
-          71.2824
+          -122.6692,
+          39.6327,
+          -74.7781,
+          45.5175
         ]
       ]
     },
     "temporal": {
       "interval": [
         [
-          "2013-08-30T00:00:00Z",
-          "2023-12-17T00:00:00Z"
+          "2009-01-22T00:00:00Z",
+          "2024-02-09T00:00:00Z"
         ]
       ]
     }
@@ -76,7 +76,7 @@
     },
     {
       "name": "datetime",
-      "type": ["POSIXct", "POSIXt"],
+      "type": "Date",
       "description": "datetime of the observed value (ISO 8601)"
     },
     {
@@ -84,11 +84,6 @@
       "type": "character",
       "description": "temporal duration of target (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention"
     },
-    {
-      "name": "depth_m",
-      "type": "numeric",
-      "description": "depth (meters) in water column of observation"
-    },
     {
       "name": "variable",
       "type": "character",
@@ -110,49 +105,13 @@
       "title": "Test Image"
     },
     "2": {
-      "href": "https://data.ecoforecast.org/neon4cast-targets/aquatics/aquatics-targets.csv.gz",
-      "type": "application/x-parquet",
-      "title": "Aquatics Target Access",
-      "roles": [
-        "data"
-      ],
-      "description": "This R code will return results for the relevant targets file.\n\n### R\n\n```{r}\n# Use code below\n\nurl <- \"https://data.ecoforecast.org/neon4cast-targets/aquatics/aquatics-targets.csv.gz\"\ntargets <- readr::read_csv(url, show_col_types = FALSE)\n```"
-    },
-    "3": {
-      "href": "https://data.ecoforecast.org/neon4cast-targets/terrestrial/terrestrial-targets.csv.gz",
-      "type": "application/x-parquet",
-      "title": "Terrestrial Target Access",
-      "roles": [
-        "data"
-      ],
-      "description": "This R code will return results for the relevant targets file.\n\n### R\n\n```{r}\n# Use code below\n\nurl <- \"https://data.ecoforecast.org/neon4cast-targets/terrestrial/terrestrial-targets.csv.gz\"\ntargets <- readr::read_csv(url, show_col_types = FALSE)\n```"
-    },
-    "4": {
-      "href": "https://data.ecoforecast.org/neon4cast-targets/beetles/beetles-targets.csv.gz",
-      "type": "application/x-parquet",
-      "title": "Beetles Target Access",
-      "roles": [
-        "data"
-      ],
-      "description": "This R code will return results for the relevant targets file.\n\n### R\n\n```{r}\n# Use code below\n\nurl <- \"https://data.ecoforecast.org/neon4cast-targets/beetles/beetles-targets.csv.gz\"\ntargets <- readr::read_csv(url, show_col_types = FALSE)\n```"
-    },
-    "5": {
-      "href": "https://data.ecoforecast.org/neon4cast-targets/phenology/phenology-targets.csv.gz",
-      "type": "application/x-parquet",
-      "title": "Phenology Target Access",
-      "roles": [
-        "data"
-      ],
-      "description": "This R code will return results for the relevant targets file.\n\n### R\n\n```{r}\n# Use code below\n\nurl <- \"https://data.ecoforecast.org/neon4cast-targets/phenology/phenology-targets.csv.gz\"\ntargets <- readr::read_csv(url, show_col_types = FALSE)\n```"
-    },
-    "6": {
-      "href": "https://data.ecoforecast.org/neon4cast-targets/ticks/ticks-targets.csv.gz",
+      "href": "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=usgsrc4cast/duration=P1D/river-chl-targets.csv.gz",
       "type": "application/x-parquet",
-      "title": "Ticks Target Access",
+      "title": "aquatics Target Access",
       "roles": [
         "data"
       ],
-      "description": "This R code will return results for the relevant targets file.\n\n### R\n\n```{r}\n# Use code below\n\nurl <- \"https://data.ecoforecast.org/neon4cast-targets/ticks/ticks-targets.csv.gz\"\ntargets <- readr::read_csv(url, show_col_types = FALSE)\n```"
+      "description": "This R code will return results for the relevant targets file.\n\n### R\n\n```{r}\n# Use code below\n\nurl <- \"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=usgsrc4cast/duration=P1D/river-chl-targets.csv.gz\"\ntargets <- readr::read_csv(url, show_col_types = FALSE)\n```"
     }
   }
 }
diff --git a/catalog/targets/create_targets_page.R b/catalog/targets/create_targets_page.R
index e97552a517..0dd4a8f715 100644
--- a/catalog/targets/create_targets_page.R
+++ b/catalog/targets/create_targets_page.R
@@ -22,7 +22,7 @@ targets_description_create <- data.frame(project_id = 'unique project identifier
 
 #inventory_theme_df <- arrow::open_dataset(glue::glue("s3://{config$inventory_bucket}/catalog/forecasts/project_id={config$project_id}"), endpoint_override = config$endpoint, anonymous = TRUE) #|>
 
-target_url <- "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/targets/project_id=vera4cast/duration=P1D/daily-insitu-targets.csv.gz"
+target_url <- config$target_groups$aquatics$targets_file
 targets <- read_csv(target_url, show_col_types = FALSE)
 
 # inventory_theme_df <- arrow::open_dataset(arrow::s3_bucket(config$inventory_bucket, endpoint_override = config$endpoint, anonymous = TRUE))
diff --git a/dashboard/index.qmd b/dashboard/index.qmd
index 261e6c7b2c..ee2e7647a9 100644
--- a/dashboard/index.qmd
+++ b/dashboard/index.qmd
@@ -41,7 +41,7 @@ most_recent_targets <- arrow::open_csv_dataset(s3_targets,
                                     schema = arrow::schema(
                                       project_id = arrow::string(),
                                       site_id = arrow::string(),
-                                      datetime = arrow::timestamp(unit = "ns", timezone = "UTC"),
+                                      datetime = arrow::timestamp(unit = "ns"), # timezone = "UTC"),
                                       duration = arrow::string(),
                                       #depth_m = arrow::float(),
                                       variable = arrow::string(),
diff --git a/dashboard/performance.qmd b/dashboard/performance.qmd
index ac3e7410b1..33cdbda116 100644
--- a/dashboard/performance.qmd
+++ b/dashboard/performance.qmd
@@ -15,11 +15,8 @@ source("R/plot-utils.R")
 #source("../R/ignore_sigpipes.R")
 #ignore_sigpipe()
 
-terrestrial_focal_sites <- c("HARV", "OSBS")
+# TODO: update these 
 aquatics_focal_sites <- c("BARC", "CRAM")
-phenology_focal_sites <- c("HARV", "OSBS")
-ticks_focal_sites <- c("HARV", "OSBS")
-beetles_focal_sites <- c("HARV", "OSBS")
 ```
 
 This page visualizes the forecasts and forecast performance for the focal target variables.
@@ -36,18 +33,8 @@ reference_datetimes <- arrow::open_dataset("../cache/summaries") |>
   group_by(variable) |>
   dplyr::mutate(reference_datetime_max = min(c(reference_datetime_max, Sys.Date() - lubridate::days(1))))
 
-
-reference_datetimes_P1W <- arrow::open_dataset("../cache/summaries/duration=P1W") |> 
-  dplyr::mutate(wday = lubridate::wday(reference_datetime)) |> 
-  dplyr::filter(wday == 1) |> 
-  dplyr::summarize(reference_datetime_max = max(reference_datetime), .by = "variable") |>  
-  dplyr::collect() |> 
-  group_by(variable) |>
-  dplyr::mutate(reference_datetime_max = min(c(reference_datetime_max, Sys.Date() - lubridate::days(1))))
-
 config <- yaml::read_yaml("../challenge_configuration.yaml")
-sites <- readr::read_csv(paste0("../", config$site_table), show_col_types = FALSE) |> 
-  rename(site_id = field_site_id)
+sites <- readr::read_csv(paste0("../", config$site_table), show_col_types = FALSE)
 
 df_P1D <- arrow::open_dataset("../cache/summaries/duration=P1D") |> 
   left_join(reference_datetimes, by = "variable") |> 
@@ -59,15 +46,6 @@ df_P1D <- arrow::open_dataset("../cache/summaries/duration=P1D") |>
   filter(lubridate::as_date(datetime) > lubridate::as_date(reference_datetime)) |> 
   collect()
 
-df_P1W <- arrow::open_dataset("../cache/summaries/duration=P1W") |> 
-  left_join(reference_datetimes_P1W, by = "variable") |> 
-  filter(reference_datetime == reference_datetime_max) |> 
-  left_join(sites, by = "site_id") |> 
-  filter(site_id %in% sites$site_id,) |> 
-  mutate(reference_datetime = lubridate::as_datetime(reference_datetime),
-         datetime = lubridate::as_datetime(datetime)) |> 
-  filter(lubridate::as_date(datetime) > lubridate::as_date(reference_datetime)) |> 
-  collect()
 ```
 
 ```{r}
@@ -83,20 +61,11 @@ df_P1D_scores <- arrow::open_dataset("../cache/scores/duration=P1D") |>
 cutoff <- Sys.Date() - lubridate::days(365)
 
 
-df_P1W_scores <- arrow::open_dataset("../cache/scores/duration=P1W") |> 
-  left_join(sites, by = "site_id") |> 
-  mutate(reference_datetime = lubridate::as_datetime(reference_datetime),
-         datetime = lubridate::as_datetime(datetime)) |> 
-  filter(reference_datetime > cutoff) |> 
-  collect() 
-
 ref <- Sys.Date() - lubridate::days(30)
 
 ref_P1D <- min(c(Sys.Date() - lubridate::days(30), 
              lubridate::as_date(df_P1D$reference_datetime)))
 
-ref_P1W <- max(c(Sys.Date() - lubridate::days(365), 
-             lubridate::as_date(df_P1W$reference_datetime)))
 
 #n_data <- 10
 #who <- combined |> filter(!is.na(observation)) |> summarise(has_data = max(reference_datetime)) |> collect()
@@ -105,10 +74,6 @@ ex_P1D <- df_P1D_scores |>
   mutate(min_reference_datetime = min(reference_datetime)) |> 
   filter(reference_datetime == min_reference_datetime)
 
-ex_P1W <- df_P1W_scores |> 
-  mutate(min_reference_datetime = min(reference_datetime)) |> 
-  filter(reference_datetime == min_reference_datetime)
-
 ```
 
 ```{r}
@@ -120,114 +85,13 @@ best_P1D_scores <- df_P1D_scores |>
   group_by(variable) |> 
   slice(1:5)
 
-best_P1W_scores <- df_P1W_scores |> 
-  summarise(score = mean(crps, na.rm = TRUE), .by = c("model_id","variable")) |> 
-  filter(!is.infinite(score)) |> 
-  arrange(variable, score) |> 
-  group_by(variable) |> 
-  slice(1:5)
 ```
 Forecasts submitted on `r max(lubridate::as_date(df_P1D$reference_datetime))`
 
 
-::: panel-tabset
-
-### Terrestrial: Net Ecosystem Exchange
-
-Forecast summaries are available [here](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/summaries/Terrestrial/Daily_Net_ecosystem_exchange/collection.json)
-
-```{r}
-best_models <- best_P1D_scores |> filter(variable == "nee") |> pull(model_id)
-
-df_P1D |> 
-  filter(variable == "nee",
-         model_id %in% best_models, 
-         site_id %in% terrestrial_focal_sites) |> 
-  mutate(observation = as.numeric(NA)) |> 
-  forecast_plots()
-```
-
-### Terrestrial: Latent Heat Flux
-
-Forecast summaries are available [here](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/summaries/Terrestrial/Daily_latent_heat_flux/collection.json)
-
-```{r}
-best_models <- best_P1D_scores |> filter(variable == "le") |> pull(model_id)
-
-df_P1D |> 
-  filter(variable == "le",
-         model_id %in% best_models,
-         site_id %in% terrestrial_focal_sites) |> 
-  mutate(observation = as.numeric(NA)) |> 
-  forecast_plots()
-```
-### Phenology: Greeness
-
-Forecast summaries are available [here](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json)
-
-```{r}
-best_models <- best_P1D_scores |> filter(variable == "gcc_90") |> pull(model_id)
-
-df_P1D |> 
-  filter(variable == "gcc_90",
-         model_id %in% best_models, 
-         site_id %in% phenology_focal_sites) |> 
-  mutate(observation = as.numeric(NA)) |> 
-  forecast_plots()
-```
-
-
-### Phenology: Redness
-
-Forecast summaries are available [here](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json)
-
-```{r}
-best_models <- best_P1D_scores |> filter(variable == "rcc_90") |> pull(model_id)
-
-
-df_P1D |> 
-  filter(variable == "rcc_90",
-         model_id %in% best_models, 
-         site_id %in% phenology_focal_sites) |> 
-  mutate(observation = as.numeric(NA)) |> 
-  forecast_plots()
-```
-
-### Aquatics: water temperature
-
-Forecast summaries are available [here](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/summaries/Aquatics/Daily_Water_temperature/collection.json)
-
-```{r}
-best_models <- best_P1D_scores |> filter(variable == "temperature") |> pull(model_id)
-
-
-
-df_P1D |> 
-  filter(variable == c("temperature"),
-         model_id %in% best_models,
-         site_id %in% aquatics_focal_sites) |> 
-  mutate(observation = as.numeric(NA)) |> 
-  forecast_plots()
-```
-
-### Aquatics: dissolved oxygen
-
-Forecast summaries are available [here](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json)
-
-```{r}
-best_models <- best_P1D_scores |> filter(variable == "oxygen") |> pull(model_id)
-
-df_P1D |> 
-  filter(variable == c("oxygen"),
-         model_id %in% best_models,
-         site_id %in% aquatics_focal_sites) |> 
-  mutate(observation = as.numeric(NA)) |> 
-  forecast_plots()
-```
-
 ### Aquatics: Chlorophyll-a
 
-Forecast summaries are available [here](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/summaries/Aquatics/Daily_Chlorophyll_a/collection.json)
+Forecast summaries are available [here](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/usgsrc4cast-ci/main/catalog/summaries/Aquatics/Daily_Chlorophyll_a/collection.json)
 
 ```{r}
 best_models <- best_P1D_scores |> filter(variable == "chla") |> pull(model_id)
@@ -235,140 +99,21 @@ best_models <- best_P1D_scores |> filter(variable == "chla") |> pull(model_id)
 df_P1D |> 
   filter(variable == c("chla"),
          model_id %in% best_models, 
-         site_id %in% aquatics_focal_sites) |> 
+         # TODO: update these 
+         # site_id %in% aquatics_focal_sites
+         ) |> 
   mutate(observation = as.numeric(NA)) |> 
   forecast_plots()
 
 ```
 
-### Beetle community richness
-
-Forecast summaries are available [here](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/summaries/Beetles/Weekly_beetle_community_richness/collection.json)
-
-```{r}
-best_models <- best_P1W_scores |> filter(variable == "richness") |> pull(model_id)
-
-
-df_P1W |> 
-  filter(variable == c("richness"),
-         model_id %in% best_models,
-         site_id %in% beetles_focal_sites) |> 
-  mutate(observation = as.numeric(NA)) |> 
-  forecast_plots()
-```
-
-### Beetle community abundance
-
-Forecast summaries are available [here](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/summaries/Beetles/Weekly_beetle_community_abundance/collection.json)
-
-```{r}
-best_models <- best_P1W_scores |> filter(variable == "abundance") |> pull(model_id)
-
-df_P1W |> 
-  filter(variable == c("abundance"),
-         model_id %in% best_models,
-         site_id %in% beetles_focal_sites) |> 
-  mutate(observation = as.numeric(NA)) |> 
-  forecast_plots()
-```
-
-
-### Ticks: Amblyomma americanum
-
-Forecast summaries are available [here](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/summaries/Ticks/collection.json)
-
-```{r}
-best_models <- best_P1W_scores |> filter(variable == "amblyomma_americanum") |> pull(model_id)
-
-df_P1W |> 
-  filter(variable == c("amblyomma_americanum"),
-         model_id %in% best_models,
-         site_id %in% ticks_focal_sites) |> 
-  mutate(observation = as.numeric(NA)) |> 
-  forecast_plots()
-```
-
 :::
 
 ## Forecast analysis
 
-Below are forecasts submitted 30 days ago and include the observations used to evaluate them. Mouse over to see the team id, scroll to zoom.  Only the top five performing models are shown. Information on how to access the scores can be found in our [catalog](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/scores/collection.json)
+Below are forecasts submitted 30 days ago and include the observations used to evaluate them. Mouse over to see the team id, scroll to zoom.  Only the top five performing models are shown. Information on how to access the scores can be found in our [catalog](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/usgsrc4cast-ci/main/catalog/scores/collection.json)
 
 ::: panel-tabset
-### Terrestrial: Net Ecosystem Exchange
-
-```{r}
-best_models <- best_P1D_scores |> filter(variable == "nee") |> pull(model_id)
-
-ex_P1D |> 
-  filter(variable == "nee",
-         model_id %in% best_models, 
-         site_id %in% terrestrial_focal_sites) |> 
-  forecast_plots()
-```
-
-### Terrestrial: Latent Heat Flux
-
-```{r}
-best_models <- best_P1D_scores |> filter(variable == "le") |> pull(model_id)
-
-
-ex_P1D |> 
-  filter(variable == "le",
-         model_id %in% best_models,
-         site_id %in% terrestrial_focal_sites) |> 
-  forecast_plots()
-```
-### Phenology: Greeness
-
-```{r}
-best_models <- best_P1D_scores |> filter(variable == "gcc_90") |> pull(model_id)
-
-
-ex_P1D |> 
-  filter(variable == "gcc_90",
-         model_id %in% best_models,
-         site_id %in% phenology_focal_sites) |> 
-  forecast_plots()
-```
-
-
-### Phenology: Redness
-
-```{r}
-best_models <- best_P1D_scores |> filter(variable == "rcc_90") |> pull(model_id)
-
-
-ex_P1D |> 
-  filter(variable == "rcc_90",
-         model_id %in% best_models,
-         site_id %in% phenology_focal_sites) |> 
-  forecast_plots()
-```
-
-### Aquatics: water temperature
-
-```{r}
-best_models <- best_P1D_scores |> filter(variable == "temperature") |> pull(model_id)
-
-ex_P1D |> 
-  filter(variable == c("temperature"),
-         model_id %in% best_models,
-         site_id %in% aquatics_focal_sites) |> 
-  forecast_plots()
-```
-
-### Aquatics: disolved oxygen
-
-```{r}
-best_models <- best_P1D_scores |> filter(variable == "oxygen") |> pull(model_id)
-
-ex_P1D |> 
-  filter(variable == c("oxygen"),
-         model_id %in% best_models,
-         site_id %in% aquatics_focal_sites) |> 
-  forecast_plots()
-```
 
 ### Aquatics: chrophyll-a
 
@@ -379,52 +124,13 @@ best_models <- best_P1D_scores |> filter(variable == "chla") |> pull(model_id)
 ex_P1D |> 
   filter(variable == c("chla"),
          model_id %in% best_models, 
-         site_id %in% aquatics_focal_sites) |> 
+         #TODO: Update 
+         # site_id %in% aquatics_focal_sites
+         ) |> 
   forecast_plots()
 
 ```
 
-### Beetle community richness
-
-```{r}
-best_models <- best_P1W_scores |> filter(variable == "richness") |> pull(model_id)
-
-
-ex_P1W |> 
-  filter(variable == c("richness"),
-         model_id %in% best_models,
-         site_id %in% beetles_focal_sites) |> 
-  forecast_plots()
-```
-
-### Beetle community abundance
-
-```{r}
-best_models <- best_P1W_scores |> filter(variable == "abundance") |> pull(model_id)
-
-
-ex_P1W |> 
-  filter(variable == c("abundance"),
-         model_id %in% best_models,
-         site_id %in% beetles_focal_sites) |> 
-  forecast_plots()
-```
-
-
-### Ticks: Amblyomma americanum
-
-```{r}
-best_models <- best_P1W_scores |> filter(variable == "amblyomma_americanum") |> pull(model_id)
-
-
-ex_P1W |> 
-  filter(variable == c("amblyomma_americanum"),
-         model_id %in% best_models,
-         site_id %in% ticks_focal_sites) |> 
-  forecast_plots()
-```
-
-
 :::
 
 ## Aggregated scores
@@ -439,65 +145,10 @@ Learn about the continous ranked probablity score [here](https://projects.ecofor
 
 ::: panel-tabset
 
-### Terrestrial: Net Ecosystem Exchange
-
-```{r}
-leaderboard_plots(df_P1D_scores, "nee")
-```
-
-### Terrestrial: Latent Heat Flux
-
-```{r}
-leaderboard_plots(df_P1D_scores, "le")
-```
-### Phenology: Greeness
-
-```{r}
-leaderboard_plots(df_P1D_scores, "gcc_90")
-```
-
-
-### Phenology: Redness
-
-```{r}
-leaderboard_plots(df_P1D_scores, "rcc_90")
-```
-
-### Aquatics: water temperature
-
-```{r}
-leaderboard_plots(df_P1D_scores, "temperature")
-```
-
-### Aquatics: dissolved oxygen
-
-```{r}
-leaderboard_plots(df_P1D_scores, "oxygen")
-```
-
 ### Aquatics: chrophyll-a
 
 ```{r}
 leaderboard_plots(df_P1D_scores, "chla")
 ```
 
-### Beetle community richness
-
-```{r}
-leaderboard_plots(df_P1W_scores, "richness")
-```
-
-### Beetle community abundance
-
-```{r}
-leaderboard_plots(df_P1W_scores, "abundance")
-```
-
-
-### Ticks: Amblyomma americanum
-
-```{r}
-leaderboard_plots(df_P1W_scores, "amblyomma_americanum")
-```
-
 :::
diff --git a/dashboard/targets.qmd b/dashboard/targets.qmd
index 162bdca951..8f7303c1db 100644
--- a/dashboard/targets.qmd
+++ b/dashboard/targets.qmd
@@ -11,6 +11,7 @@ aquatics_focal_sites <- c("BARC", "CRAM")
 ```
 
 ```{r message=FALSE, echo = FALSE}
+# TODO: need to update this 
 googlesheets4::gs4_deauth()
 target_metadata <- googlesheets4::read_sheet("https://docs.google.com/spreadsheets/d/10YTX9ae_C1rFdLgEDkUcCRCpUkVYv06leY01BtD1BgM/edit?usp=sharing")
 ```
@@ -23,112 +24,36 @@ target_metadata <- target_metadata |>
 
 ## tl;dr: Forecast the targets!
 
-The "targets" are time-series of National Ecological Observatory Network ([NEON](https://www.neonscience.org)) data for use in model development and forecast evaluation.
+The "targets" are time-series of United States Geological Survey ([USGS](https://www.usgs.gov/)) data for use in model development and forecast evaluation.
 
-The targets are updated as new NEON data is made available.
+The targets are updated as new USGS data are made available.
 
-The 10 targets were specifically chosen to include ecosystem, community, and population dynamics and are represented by five "themes".
-The themes and links to targets files are included below.
+This challenge focuses on forecasting river chlorophyll-a at select USGS monitoring locations. The links to targets files are included below.
 
 ## Where to start {#sec-starting-sites}
 
-If are you are getting started, we recommend the following focal sites for each of the five "themes".
-The first site in the list is the recommended starting site.
+<!-- If are you are getting started, we recommend the following focal sites for each of the five "themes". -->
+<!-- The first site in the list is the recommended starting site. -->
 
--   Terrestrial: `r terrestrial_focal_sites`
--   Aquatics: `r aquatics_focal_sites`
--   Phenology: `r phenology_focal_sites`
--   Beetles: `r beetles_focal_sites`
--   Ticks: `r ticks_focal_sites`
+<!-- -   Aquatics: `r aquatics_focal_sites` -->
 
-As you develop your forecasting skills and want to expand to more sites, the targets are available at all 81 NEON sites.
+As you develop your forecasting skills and want to expand to more sites, the targets are available at all 10 USGS sites.
 You may also consider submitting forecasts to sites that match your interests.
 For example, a class being taught in the winter may be more interested in forecasting southern sites while a summer class may focus on more northern sites.
 
-More information about NEON sites can be found in the [site metadata](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-ci/main/catalog/sites/collection.json) and on NEON's [website](https://www.neonscience.org/field-sites/explore-field-sites)
+More information about USGS sites can be found in the [site metadata](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/usgs4cast-ci/main/catalog/sites/collection.json) and on USGS's [website](https://dashboard.waterdata.usgs.gov/app/nwd/en/)
 
 ## Explore the targets and themes {#sec-targets}
 
-Information on the targets files for the five "themes" is below.
+Information on the targets files for the "themes" is below.
 In the tables,
 
--   "duration" is the time-step of the variable where `PT30M` is a 30-minute mean, `P1D` is a daily mean, and `P1W` is a weekly total.
+-   "duration" is the time-step of the variable where `P1D` is a daily mean.
 
 -   The "forecast horizon" is the number of days-ahead that we want you to forecast.
 
 -   The "latency" is the time between data collection and data availability in the targets file
 
-::: panel-tabset
-### Terrestrial fluxes
-
-![](https://projects.ecoforecast.org/neon4cast-catalog/img/BONA_Twr.jpg)
-
-The exchange of water and carbon dioxide between the atmosphere and the land is akin to earth's terrestrial ecosystems breathing rate and lung capacity. 
-
-The terrestrial flux theme challenges you to forecast the gas exchange at up to 47 sites across the U.S.
-
-There are two variables and two time-steps (or duration) that you can forecast.
-
-```{r echo = FALSE}
-url_P1D <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/terrestrial_daily-targets.csv.gz"
-url_PT30M <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=PT30M/terrestrial_30min-targets.csv.gz"
-```
-
-```{r echo = FALSE}
-read_csv(c(url_P1D, url_PT30M), show_col_types = FALSE) |> 
-  distinct(variable, duration) |> 
-  left_join(target_metadata, by = c("variable","duration")) |> 
-  filter(variable %in% c("nee","le")) |> 
-  select(-class) |> 
-  rename(`forecast horizon` = horizon) |> 
-  knitr::kable()
-
-```
-
-#### Daily mean
-
-The daily mean target file is located at the following URL.
-
-```{r}
-url_P1D <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/terrestrial_daily-targets.csv.gz"
-```
-
-You can directly load it into R using the following
-
-```{r}
-terrestrial_targets <- read_csv(url_P1D, show_col_types = FALSE)
-```
-
-The file contains the following columns
-
-```{r echo = FALSE}
-terrestrial_targets |> 
-  na.omit() |> 
-  head() |> 
-  knitr::kable()
-```
-
-and the time series for the focal sites
-
-```{r}
-terrestrial_targets |> 
-  filter(site_id %in% terrestrial_focal_sites) |> 
-  ggplot(aes(x = datetime, y = observation)) +
-  geom_point() +
-  facet_grid(variable~site_id, scales = "free_y") +
-  theme_bw()
-```
-
-#### 30 minute
-
-The 30 minute duration targets are designed for forecasting sub-daily carbon and water dynamics.
-The URL is found at:
-
-```{r}
-url_PT30M <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=PT30M/terrestrial_30min-targets.csv.gz"
-```
-
-Learn more at: <https://projects.ecoforecast.org/neon4cast-docs/Terrestrial.html>
 
 ### Aquatics
 
@@ -136,22 +61,23 @@ Learn more at: <https://projects.ecoforecast.org/neon4cast-docs/Terrestrial.html
 
 Freshwater surface water temperature, dissolved oxygen, and chlorophyll-a all influence drinking water quality, are critical for life in aquatic environments, and can represent the health of the ecosystem.
 
-The aquatics theme challenges you to forecast daily mean water quality variables at up-to 7 lakes and 27 river/stream NEON sites.
+The aquatics theme challenges you to forecast daily mean water quality variables at up-to 10 river USGS sites.
 
 ```{r echo = FALSE}
-url <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/aquatics-targets.csv.gz"
+url <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=usgsrc4cast/duration=P1D/river-chl-targets.csv.gz"
 read_csv(url, show_col_types = FALSE) |> 
   distinct(variable, duration) |> 
-  left_join(target_metadata, by = c("variable","duration")) |> 
-  filter(variable %in% c("temperature","oxygen", "chla")) |> 
-  select(-class) |> 
+  # TODO: need to fix target_metadata 
+  # left_join(target_metadata, by = c("variable","duration")) |> 
+  filter(variable %in% c("chla")) |> 
+  # select(-class) |> 
   knitr::kable()
 ```
 
 The daily mean target file is located at the following URL.
 
 ```{r}
-url <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/aquatics-targets.csv.gz"
+url <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=usgsrc4cast/duration=P1D/river-chl-targets.csv.gz"
 ```
 
 You can directly load it into R using the following
@@ -173,183 +99,14 @@ and the time series for the focal sites
 
 ```{r}
 aquatics_targets |> 
-  filter(site_id %in% aquatics_focal_sites) |> 
-  ggplot(aes(x = datetime, y = observation)) +
-  geom_point() +
-  facet_grid(variable~site_id, scales = "free_y") +
-  theme_bw()
-```
-
-Water temperature at multiple depths measured at the UTC 00 hour are available for the 7 NEON lake sites.
-These data can be used for model development but will not be used for forecast evaluation.
-
-```{r}
-url <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/supporting_data/project_id=neon4cast/aquatics-expanded-observations.csv.gz"
-```
-
-Learn more at: <https://projects.ecoforecast.org/neon4cast-docs/Aquatics.html>
-
-### Phenology
-
-![](https://phenocam.nau.edu/data/latest/NEON.D01.BART.DP1.00033.jpg)
-
-Phenology (the changes in plant canopies over the year) has been identified as one of the primary ecological fingerprints of global climate change.
-
-The greenness and redness, as measured by a camera looking down at the top of vegetation are a quantitative measure of phenology.
-The phenology theme challenges you to forecast daily mean greeness and/or redness at up-to 47 terrestrial NEON sites.
-
-```{r}
-url <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/phenology-targets.csv.gz"
-read_csv(url, show_col_types = FALSE) |> 
-  distinct(variable, duration) |> 
-  left_join(target_metadata, by = c("variable","duration")) |> 
-  filter(variable %in% c("gcc_90","rcc_90")) |> 
-  select(-class) |> 
-  knitr::kable()
-```
-
-The daily mean target file is located at the following URL.
-
-```{r}
-url <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1D/phenology-targets.csv.gz"
-```
-
-You can directly load it into R using the following
-
-```{r}
-phenology_targets <- read_csv(url, show_col_types = FALSE)
-```
-
-The file contains the following columns
-
-```{r echo = FALSE}
-phenology_targets |> 
-  na.omit() |> 
-  head() |> 
-  knitr::kable()
-```
-
-and the time series for the focal sites
-
-```{r}
-phenology_targets |> 
-  filter(site_id %in% phenology_focal_sites) |> 
-  ggplot(aes(x = datetime, y = observation)) +
-  geom_point() +
-  facet_grid(variable~site_id, scales = "free_y") +
-  theme_bw()
-
-```
-
-Learn more at: <https://projects.ecoforecast.org/neon4cast-docs/Phenology.html>
-
-### Beetle communities
-
-![](https://www.neonscience.org/sites/default/files/styles/max_width_1170px/public/image-content-images/Beetles_pinned.jpg)
-
-Sentinel species (such as beetles) can give forewarning of environmental risk to humans, so are particularly useful for such monitoring and forecasting efforts because they can provide surrogates for other co-located components of biodiversity.
-
-The beetles theme challenges you to forecast weekly ground beetles (Family: Carabidae) abundance and richness (two measures of biodiversity) at up-to 47 terrestrial NEON sites.
-
-```{r echo = FALSE}
-url <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/beetles-targets.csv.gz"
-read_csv(url, show_col_types = FALSE) |> 
-  distinct(variable, duration) |> 
-  left_join(target_metadata, by = c("variable","duration")) |> 
-  filter(variable %in% c("abundance","richness")) |> 
-  select(-class) |> 
-  knitr::kable()
-```
-
-The daily mean target file is located at the following URL.
-
-```{r}
-url <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/beetles-targets.csv.gz"
-```
-
-You can directly load it into R using the following
-
-```{r}
-beetles_targets <- read_csv(url, show_col_types = FALSE)
-```
-
-The file contains the following columns
-
-```{r echo = FALSE}
-beetles_targets |> 
-  na.omit() |> 
-  head() |> 
-  knitr::kable()
-```
-
-and the time series for the focal sites
-
-```{r}
-beetles_targets |> 
-  filter(site_id %in% beetles_focal_sites) |> 
-  ggplot(aes(x = datetime, y = observation)) +
-  geom_point() +
-  facet_grid(variable~site_id, scales = "free_y") +
-  theme_bw()
-```
-
-Learn more at: <https://projects.ecoforecast.org/neon4cast-docs/Beetles.html>
-
-### Tick populations
-
-![](https://www.neonscience.org/sites/default/files/styles/max_2600x2600/public/image-content-images/tick-image.jpg)
-
-Target species for the tick population forecasts are Amblyomma americanum nymphal ticks.
-A.
-americanum is a vector of ehrlichiosis, tularemia, and southern tick-associated rash illness.
-The species is present in the eastern United States, and their populations are expanding.
-There is a correlation between tick population abundance and disease incidence, meaning forecasts for tick abundance have the potential to aid in our understanding of disease risk through time and space.
-
-The beetles theme challenges you to forecast weekly Amblyomma americanum nymphal tick abundance at up-to 9 terrestrial NEON sites.
-
-```{r echo = FALSE}
-url <- "https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/ticks-targets.csv.gz"
-read_csv(url, show_col_types = FALSE) |> 
-  distinct(variable, duration) |> 
-  left_join(target_metadata, by = c("variable","duration")) |> 
-  filter(variable %in% c("amblyomma_americanum")) |>  select(-class) |> 
-  knitr::kable()
-```
-
-The weekly target file is located at the following URL.
-
-```{r}
-"https://sdsc.osn.xsede.org/bio230014-bucket01/challenges/targets/project_id=neon4cast/duration=P1W/ticks-targets.csv.gz"
-```
-
-You can directly load it into R using the following
-
-```{r}
-ticks_targets <- read_csv(url, show_col_types = FALSE)
-```
-
-The file contains the following columns
-
-```{r echo = FALSE}
-ticks_targets |> 
-  na.omit() |> 
-  head() |> 
-  knitr::kable()
-```
-
-and the time series for the focal sites
-
-```{r}
-ticks_targets |> 
-  filter(site_id %in% ticks_focal_sites) |> 
+  # TODO: need to update focal sites 
+  # filter(site_id %in% aquatics_focal_sites) |> 
   ggplot(aes(x = datetime, y = observation)) +
   geom_point() +
   facet_grid(variable~site_id, scales = "free_y") +
   theme_bw()
 ```
 
-Learn more at: <https://projects.ecoforecast.org/neon4cast-docs/Ticks.html>
-:::
 
 ## Explore the sites
 
@@ -368,13 +125,12 @@ leaflet() %>%
   addMarkers(data  = sites, popup=~as.character(site_id), group = ~as.character(Partner), clusterOptions = markerClusterOptions())
 ```
 
-<br /> <br /> The following table lists all the sites in the NEON Ecological Forecasting Challenge.
+<br /> <br /> The following table lists all the sites in the EFI-USGS Ecological Forecasting Challenge.
 The columns with "theme" names incidate whether that site is included in that theme's target file.
 
 ```{r echo = FALSE}
-site_list <- read_csv("../neon4cast_field_site_metadata.csv", show_col_types = FALSE) |> 
-  rename(site_id = field_site_id) |> 
-  select(site_id, field_site_name, terrestrial, aquatics, phenology, ticks, beetles) 
+site_list <- read_csv("../USGS_site_metadata.csv", show_col_types = FALSE) |> 
+  select(site_id, site_no, station_nm, site_url) 
 ```
 
 ```{r echo = FALSE}
diff --git a/submission_processing/process_submissions.R b/submission_processing/process_submissions.R
index a07dbd57af..61551d4e6c 100644
--- a/submission_processing/process_submissions.R
+++ b/submission_processing/process_submissions.R
@@ -37,211 +37,223 @@ local_dir <- file.path(here::here(), "submissions")
 unlink(local_dir, recursive = TRUE)
 fs::dir_create(local_dir)
 
-message("Downloading forecasts ...")
 
-minioclient::mc_mirror(from = fs::path("submit", config$submissions_bucket, config$project_id),
-                       to = local_dir)
+## see if there are any files to download and process
+submit_files = minioclient::mc_ls(target = fs::path("submit", config$submissions_bucket, config$project_id),
+                                  recursive = TRUE,
+                                  details = TRUE)
 
-submissions <- fs::dir_ls(local_dir,
-                          recurse = TRUE,
-                          type = "file")
-submissions_filenames <- basename(submissions)
-print(submissions)
+if(nrow(submit_files) > 0){
+  message("Downloading forecasts ...")
 
-if(length(submissions) > 0){
+  minioclient::mc_mirror(from = fs::path("submit", config$submissions_bucket, config$project_id),
+                         to = local_dir)
 
-  Sys.unsetenv("AWS_DEFAULT_REGION")
-  Sys.unsetenv("AWS_S3_ENDPOINT")
-  Sys.setenv(AWS_EC2_METADATA_DISABLED="TRUE")
+  submissions <- fs::dir_ls(local_dir,
+                            recurse = TRUE,
+                            type = "file")
+  submissions_filenames <- basename(submissions)
+  print(submissions)
 
-  s3 <- arrow::s3_bucket(config$forecasts_bucket,
-                         endpoint_override = config$endpoint,
-                         access_key = Sys.getenv("OSN_KEY"),
-                         secret_key = Sys.getenv("OSN_SECRET"))
+  if(length(submissions) > 0){
+
+    Sys.unsetenv("AWS_DEFAULT_REGION")
+    Sys.unsetenv("AWS_S3_ENDPOINT")
+    Sys.setenv(AWS_EC2_METADATA_DISABLED="TRUE")
+
+    s3 <- arrow::s3_bucket(config$forecasts_bucket,
+                           endpoint_override = config$endpoint,
+                           access_key = Sys.getenv("OSN_KEY"),
+                           secret_key = Sys.getenv("OSN_SECRET"))
+
+    s3_scores <- arrow::s3_bucket(file.path(config$scores_bucket,"parquet"),
+                                  endpoint_override = config$endpoint,
+                                  access_key = Sys.getenv("OSN_KEY"),
+                                  secret_key = Sys.getenv("OSN_SECRET"))
+
+
+    s3_inventory <- arrow::s3_bucket(dirname(config$inventory_bucket),
+                                     endpoint_override = config$endpoint,
+                                     access_key = Sys.getenv("OSN_KEY"),
+                                     secret_key = Sys.getenv("OSN_SECRET"))
+
+    s3_inventory$CreateDir(paste0("inventory/catalog/forecasts/project_id=", config$project_id))
+
+    s3_inventory <- arrow::s3_bucket(paste0(config$inventory_bucket,
+                                            "/catalog/forecasts/project_id=",
+                                            config$project_id),
+                                     endpoint_override = config$endpoint,
+                                     access_key = Sys.getenv("OSN_KEY"),
+                                     secret_key = Sys.getenv("OSN_SECRET"))
+
+    inventory_df <- arrow::open_dataset(s3_inventory) |> dplyr::collect()
+
+    time_stamp <- format(Sys.time(), format = "%Y%m%d%H%M%S")
+
+    print(inventory_df)
+
+    for(i in 1:length(submissions)){
+
+      curr_submission <- basename(submissions[i])
+      theme <-  stringr::str_split(curr_submission, "-")[[1]][1]
+      file_name_model_id <-  stringr::str_split(tools::file_path_sans_ext(tools::file_path_sans_ext(curr_submission)), "-")[[1]][5]
+      file_name_reference_datetime <- lubridate::as_datetime(paste0(stringr::str_split(curr_submission, "-")[[1]][2:4], collapse = "-"))
+      submission_dir <- dirname(submissions[i])
+      print(curr_submission)
+
+      if((tools::file_ext(curr_submission) %in% c("gz", "csv", "nc"))){
+
+        valid <- forecast_output_validator(file.path(local_dir, curr_submission))
+
+        if(valid){
+
+          # still OK to use read4cast as there aren't challenge-specific things
+          # in the package, other than list of all potential target variables,
+          # which could be updated if we forecast new variables (but for usgsrc4cast we're forecasting chla)
+          fc <- read4cast::read_forecast(submissions[i])
+
+          pub_datetime <- strftime(Sys.time(), format = "%Y-%m-%d %H:%M:%S", tz = "UTC")
+
+          if(!"duration" %in% names(fc)){
+            # if(theme == "terrestrial_30min"){
+            #   fc <- fc |> dplyr::mutate(duration = "PT30M")
+            # }else if(theme %in% c("ticks","beetles")){
+            #   fc <- fc |> dplyr::mutate(duration = "P1W")
+            # }else if(theme %in% c("aquatics","phenology","terrestrial_daily")){
+            #   fc <- fc |> dplyr::mutate(duration = "P1D")
+            # }else{
+            # if(stringr::str_detect(fc$datetime[1], ":")){
+            #   fc <- fc |> dplyr::mutate(duration = "P1H")
+            # }else{
+            fc <- fc |> dplyr::mutate(duration = "P1D") # currently only have "P1D" duration for usgsrc4cast
+            # }
+          }
+
+
+          if(!("model_id" %in% colnames(fc))){
+            fc <- fc |> mutate(model_id = file_name_model_id)
+          }else if(fc$model_id[1] == "null"){
+            fc <- fc |> mutate(model_id = file_name_model_id)
+          }
+
+
+          if(!("reference_datetime" %in% colnames(fc))){
+            fc <- fc |> mutate(reference_datetime = file_name_reference_datetime)
+          }
+
+          fc <- fc |>
+            dplyr::mutate(pub_datetime = lubridate::as_datetime(pub_datetime),
+                          datetime = lubridate::as_datetime(datetime),
+                          reference_datetime = lubridate::as_datetime(reference_datetime),
+                          reference_date = lubridate::as_date(reference_datetime),
+                          parameter = as.character(parameter),
+                          project_id = config$project_id) |>
+            dplyr::filter(datetime >= reference_datetime)
+
+          print(head(fc))
+          s3$CreateDir(paste0("parquet/"))
+          fc |> arrow::write_dataset(s3$path(paste0("parquet")), format = 'parquet',
+                                     partitioning = c("project_id",
+                                                      "duration",
+                                                      "variable",
+                                                      "model_id",
+                                                      "reference_date"))
+
+          s3$CreateDir(paste0("summaries"))
+          fc |>
+            dplyr::summarise(prediction = mean(prediction),
+                             .by = dplyr::any_of(c("site_id", "datetime",
+                                                   "reference_datetime", "family",
+                                                   "depth_m", "duration", "model_id",
+                                                   "parameter", "pub_datetime",
+                                                   "reference_date", "variable", "project_id"))) |>
+            score4cast::summarize_forecast(extra_groups = c("duration", "project_id", "depth_m")) |>
+            dplyr::mutate(reference_date = lubridate::as_date(reference_datetime)) |>
+            arrow::write_dataset(s3$path("summaries"), format = 'parquet',
+                                 partitioning = c("project_id",
+                                                  "duration",
+                                                  "variable",
+                                                  "model_id",
+                                                  "reference_date"))
+
+          bucket <- config$forecasts_bucket
+          curr_inventory <- fc |>
+            mutate(reference_date = lubridate::as_date(reference_datetime),
+                   date = lubridate::as_date(datetime),
+                   pub_date = lubridate::as_date(pub_datetime)) |>
+            distinct(duration, model_id, site_id, reference_date, variable, date, project_id, pub_date) |>
+            mutate(path = glue::glue("{bucket}/parquet/project_id={project_id}/duration={duration}/variable={variable}"),
+                   path_full = glue::glue("{bucket}/parquet/project_id={project_id}/duration={duration}/variable={variable}/model_id={model_id}/reference_date={reference_date}/part-0.parquet"),
+                   path_summaries = glue::glue("{bucket}/summaries/project_id={project_id}/duration={duration}/variable={variable}/model_id={model_id}/reference_date={reference_date}/part-0.parquet"),
+                   endpoint =config$endpoint)
+
+
+          curr_inventory <- dplyr::left_join(curr_inventory, sites, by = "site_id")
+
+          inventory_df <- dplyr::bind_rows(inventory_df, curr_inventory)
+
+          arrow::write_dataset(inventory_df, path = s3_inventory)
+
+          submission_timestamp <- paste0(submission_dir,"/T", time_stamp, "_", basename(submissions[i]))
+          fs::file_copy(submissions[i], submission_timestamp)
+          raw_bucket_object <- paste0("s3_store/",
+                                      config$forecasts_bucket,
+                                      "/raw/project_id=", config$project_id, "/",
+                                      basename(submission_timestamp))
+
+          minioclient::mc_cp(submission_timestamp, paste0(dirname(raw_bucket_object),"/", basename(submission_timestamp)))
+
+          if(length(minioclient::mc_ls(raw_bucket_object)) > 0){
+            minioclient::mc_rm(file.path("submit",
+                                         config$submissions_bucket,
+                                         config$project_id,
+                                         curr_submission))
+          }
+
+          rm(fc)
+          gc()
+
+        } else {
+
+          submission_timestamp <- paste0(submission_dir,"/T", time_stamp, "_", basename(submissions[i]))
+          fs::file_copy(submissions[i], submission_timestamp)
+          raw_bucket_object <- paste0("s3_store/",
+                                      config$forecasts_bucket,
+                                      "/raw/project_id=", config$project_id, "/",
+                                      basename(submission_timestamp))
+
+          minioclient::mc_cp(submission_timestamp, paste0(dirname(raw_bucket_object),"/", basename(submission_timestamp)))
+
+          if(length(minioclient::mc_ls(raw_bucket_object)) > 0){
+            minioclient::mc_rm(file.path("submit",
+                                         config$submissions_bucket,
+                                         config$project_id,
+                                         curr_submission))
+          }
 
-  s3_scores <- arrow::s3_bucket(file.path(config$scores_bucket,"parquet"),
-                                endpoint_override = config$endpoint,
-                                access_key = Sys.getenv("OSN_KEY"),
-                                secret_key = Sys.getenv("OSN_SECRET"))
-
-
-  s3_inventory <- arrow::s3_bucket(dirname(config$inventory_bucket),
-                                   endpoint_override = config$endpoint,
-                                   access_key = Sys.getenv("OSN_KEY"),
-                                   secret_key = Sys.getenv("OSN_SECRET"))
-
-  s3_inventory$CreateDir(paste0("inventory/catalog/forecasts/project_id=", config$project_id))
-
-  s3_inventory <- arrow::s3_bucket(paste0(config$inventory_bucket,
-                                          "/catalog/forecasts/project_id=",
-                                          config$project_id),
-                                   endpoint_override = config$endpoint,
-                                   access_key = Sys.getenv("OSN_KEY"),
-                                   secret_key = Sys.getenv("OSN_SECRET"))
-
-  inventory_df <- arrow::open_dataset(s3_inventory) |> dplyr::collect()
-
-  time_stamp <- format(Sys.time(), format = "%Y%m%d%H%M%S")
-
-  print(inventory_df)
-
-  for(i in 1:length(submissions)){
-
-    curr_submission <- basename(submissions[i])
-    theme <-  stringr::str_split(curr_submission, "-")[[1]][1]
-    file_name_model_id <-  stringr::str_split(tools::file_path_sans_ext(tools::file_path_sans_ext(curr_submission)), "-")[[1]][5]
-    file_name_reference_datetime <- lubridate::as_datetime(paste0(stringr::str_split(curr_submission, "-")[[1]][2:4], collapse = "-"))
-    submission_dir <- dirname(submissions[i])
-    print(curr_submission)
-
-    if((tools::file_ext(curr_submission) %in% c("gz", "csv", "nc"))){
-
-      valid <- forecast_output_validator(file.path(local_dir, curr_submission))
-
-      if(valid){
-
-        # still OK to use read4cast as there aren't challenge-specific things
-        # in the package, other than list of all potential target variables,
-        # which could be updated if we forecast new variables (but for usgsrc4cast we're forecasting chla)
-        fc <- read4cast::read_forecast(submissions[i])
-
-        pub_datetime <- strftime(Sys.time(), format = "%Y-%m-%d %H:%M:%S", tz = "UTC")
-
-        if(!"duration" %in% names(fc)){
-          # if(theme == "terrestrial_30min"){
-          #   fc <- fc |> dplyr::mutate(duration = "PT30M")
-          # }else if(theme %in% c("ticks","beetles")){
-          #   fc <- fc |> dplyr::mutate(duration = "P1W")
-          # }else if(theme %in% c("aquatics","phenology","terrestrial_daily")){
-          #   fc <- fc |> dplyr::mutate(duration = "P1D")
-          # }else{
-          # if(stringr::str_detect(fc$datetime[1], ":")){
-          #   fc <- fc |> dplyr::mutate(duration = "P1H")
-          # }else{
-          fc <- fc |> dplyr::mutate(duration = "P1D") # currently only have "P1D" duration for usgsrc4cast
-          # }
         }
+      }
+    }
 
+    message("writing inventory")
 
-        if(!("model_id" %in% colnames(fc))){
-          fc <- fc |> mutate(model_id = file_name_model_id)
-        }else if(fc$model_id[1] == "null"){
-          fc <- fc |> mutate(model_id = file_name_model_id)
-        }
-
-
-        if(!("reference_datetime" %in% colnames(fc))){
-          fc <- fc |> mutate(reference_datetime = file_name_reference_datetime)
-        }
-
-        fc <- fc |>
-          dplyr::mutate(pub_datetime = lubridate::as_datetime(pub_datetime),
-                        datetime = lubridate::as_datetime(datetime),
-                        reference_datetime = lubridate::as_datetime(reference_datetime),
-                        reference_date = lubridate::as_date(reference_datetime),
-                        parameter = as.character(parameter),
-                        project_id = config$project_id) |>
-          dplyr::filter(datetime >= reference_datetime)
-
-        print(head(fc))
-        s3$CreateDir(paste0("parquet/"))
-        fc |> arrow::write_dataset(s3$path(paste0("parquet")), format = 'parquet',
-                                   partitioning = c("project_id",
-                                                    "duration",
-                                                    "variable",
-                                                    "model_id",
-                                                    "reference_date"))
-
-        s3$CreateDir(paste0("summaries"))
-        fc |>
-          dplyr::summarise(prediction = mean(prediction),
-                           .by = dplyr::any_of(c("site_id", "datetime",
-                                                 "reference_datetime", "family",
-                                                 "depth_m", "duration", "model_id",
-                                                 "parameter", "pub_datetime",
-                                                 "reference_date", "variable", "project_id"))) |>
-          score4cast::summarize_forecast(extra_groups = c("duration", "project_id", "depth_m")) |>
-          dplyr::mutate(reference_date = lubridate::as_date(reference_datetime)) |>
-          arrow::write_dataset(s3$path("summaries"), format = 'parquet',
-                               partitioning = c("project_id",
-                                                "duration",
-                                                "variable",
-                                                "model_id",
-                                                "reference_date"))
-
-        bucket <- config$forecasts_bucket
-        curr_inventory <- fc |>
-          mutate(reference_date = lubridate::as_date(reference_datetime),
-                 date = lubridate::as_date(datetime),
-                 pub_date = lubridate::as_date(pub_datetime)) |>
-          distinct(duration, model_id, site_id, reference_date, variable, date, project_id, pub_date) |>
-          mutate(path = glue::glue("{bucket}/parquet/project_id={project_id}/duration={duration}/variable={variable}"),
-                 path_full = glue::glue("{bucket}/parquet/project_id={project_id}/duration={duration}/variable={variable}/model_id={model_id}/reference_date={reference_date}/part-0.parquet"),
-                 path_summaries = glue::glue("{bucket}/summaries/project_id={project_id}/duration={duration}/variable={variable}/model_id={model_id}/reference_date={reference_date}/part-0.parquet"),
-                 endpoint =config$endpoint)
-
-
-        curr_inventory <- dplyr::left_join(curr_inventory, sites, by = "site_id")
-
-        inventory_df <- dplyr::bind_rows(inventory_df, curr_inventory)
-
-        arrow::write_dataset(inventory_df, path = s3_inventory)
-
-        submission_timestamp <- paste0(submission_dir,"/T", time_stamp, "_", basename(submissions[i]))
-        fs::file_copy(submissions[i], submission_timestamp)
-        raw_bucket_object <- paste0("s3_store/",
-                                    config$forecasts_bucket,
-                                    "/raw/project_id=", config$project_id, "/",
-                                    basename(submission_timestamp))
-
-        minioclient::mc_cp(submission_timestamp, paste0(dirname(raw_bucket_object),"/", basename(submission_timestamp)))
-
-        if(length(minioclient::mc_ls(raw_bucket_object)) > 0){
-          minioclient::mc_rm(file.path("submit",
-                                       config$submissions_bucket,
-                                       config$project_id,
-                                       curr_submission))
-        }
-
-        rm(fc)
-        gc()
-
-      } else {
-
-        submission_timestamp <- paste0(submission_dir,"/T", time_stamp, "_", basename(submissions[i]))
-        fs::file_copy(submissions[i], submission_timestamp)
-        raw_bucket_object <- paste0("s3_store/",
-                                    config$forecasts_bucket,
-                                    "/raw/project_id=", config$project_id, "/",
-                                    basename(submission_timestamp))
+    arrow::write_dataset(inventory_df, path = s3_inventory)
 
-        minioclient::mc_cp(submission_timestamp, paste0(dirname(raw_bucket_object),"/", basename(submission_timestamp)))
+    s3_inventory <- arrow::s3_bucket(paste0(config$inventory_bucket),
+                                     endpoint_override = config$endpoint,
+                                     access_key = Sys.getenv("OSN_KEY"),
+                                     secret_key = Sys.getenv("OSN_SECRET"))
 
-        if(length(minioclient::mc_ls(raw_bucket_object)) > 0){
-          minioclient::mc_rm(file.path("submit",
-                                       config$submissions_bucket,
-                                       config$project_id,
-                                       curr_submission))
-        }
+    inventory_df |> dplyr::distinct(model_id, project_id) |>
+      arrow::write_csv_arrow(s3_inventory$path("model_id/model_id-project_id-inventory.csv"))
 
-      }
-    }
   }
 
-  message("writing inventory")
-
-  arrow::write_dataset(inventory_df, path = s3_inventory)
-
-  s3_inventory <- arrow::s3_bucket(paste0(config$inventory_bucket),
-                                   endpoint_override = config$endpoint,
-                                   access_key = Sys.getenv("OSN_KEY"),
-                                   secret_key = Sys.getenv("OSN_SECRET"))
-
-  inventory_df |> dplyr::distinct(model_id, project_id) |>
-    arrow::write_csv_arrow(s3_inventory$path("model_id/model_id-project_id-inventory.csv"))
+  unlink(local_dir, recursive = TRUE)
 
+  message(paste0("Completed Processing Submissions ", Sys.time()))
+}else{
+  message("No submitted files to process")
 }
 
-unlink(local_dir, recursive = TRUE)
 
-message(paste0("Completed Processing Submissions ", Sys.time()))