From 8b41c288d6caf5ee96fa992fa118840b03b49a60 Mon Sep 17 00:00:00 2001 From: github-actions Date: Wed, 6 Mar 2024 05:11:11 +0000 Subject: [PATCH] update pages --- index.html | 6 +++--- performance.html | 16 ++++++++-------- search.json | 8 ++++---- targets.html | 4 ++-- .../figure-html/unnamed-chunk-8-1.png | Bin 83828 -> 84085 bytes 5 files changed, 17 insertions(+), 17 deletions(-) diff --git a/index.html b/index.html index 1e001c7cee..20f8468279 100644 --- a/index.html +++ b/index.html @@ -198,7 +198,7 @@

Why a forecast

Total forecasts submitted to the EFI-USGS Challenge

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77

+

80

@@ -215,7 +215,7 @@

Why a forecast

Most recent data for model training

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2024-03-05
+
2024-03-06
@@ -272,7 +272,7 @@

Contact

Acknowledgements

-

Thomas, R. Q., Boettiger, C., Carey, C. C., Dietze, M. C., Johnson, L. R., Kenney, M. A., et al. (2023). The NEON Ecological Forecasting Challenge. Frontiers in Ecology and the Environment, 21(3), 112–113. https://doi.org/10.1002/fee.2616

We thank the EFI community for feedback on the design of the Challenge. This material is based upon work supported by the National Science Foundation under Grant DEB-1926388.

Page last updated on 2024-03-05

+

Thomas, R. Q., Boettiger, C., Carey, C. C., Dietze, M. C., Johnson, L. R., Kenney, M. A., et al. (2023). The NEON Ecological Forecasting Challenge. Frontiers in Ecology and the Environment, 21(3), 112–113. https://doi.org/10.1002/fee.2616

We thank the EFI community for feedback on the design of the Challenge. This material is based upon work supported by the National Science Foundation under Grant DEB-1926388.

Page last updated on 2024-03-06

diff --git a/performance.html b/performance.html index c8b9efe9d6..613d69075f 100644 --- a/performance.html +++ b/performance.html @@ -188,14 +188,14 @@

On this page

Most recent forecasts

Only the top performing models from the last 30 days are shown.

-

Forecasts submitted on 2024-03-04

+

Forecasts submitted on 2024-03-05

Aquatics: Chlorophyll-a

Forecast summaries are available here

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- +
+

:::

@@ -210,8 +210,8 @@

Forecast analysis

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- +
+
@@ -224,7 +224,7 @@

Aggregated scores

Scores are shown by reference date and forecast horizon (in days).

-

Scores are averaged across all submissions of the model with a given horizon or a given reference_datetime using submissions made since 2023-03-06.
+

Scores are averaged across all submissions of the model with a given horizon or a given reference_datetime using submissions made since 2023-03-07.

Learn about the continous ranked probablity score here

@@ -233,8 +233,8 @@

Aggregated scores

-
- +
+
diff --git a/search.json b/search.json index 744bf2ebb4..5db390c464 100644 --- a/search.json +++ b/search.json @@ -11,7 +11,7 @@ "href": "performance.html#sec-performance", "title": "Forecast performance", "section": "Most recent forecasts", - "text": "Most recent forecasts\nOnly the top performing models from the last 30 days are shown.\nForecasts submitted on 2024-03-04\n\nAquatics: Chlorophyll-a\nForecast summaries are available here\n\n\n\n\n\n\n:::" + "text": "Most recent forecasts\nOnly the top performing models from the last 30 days are shown.\nForecasts submitted on 2024-03-05\n\nAquatics: Chlorophyll-a\nForecast summaries are available here\n\n\n\n\n\n\n:::" }, { "objectID": "performance.html#forecast-analysis", @@ -25,7 +25,7 @@ "href": "performance.html#aggregated-scores", "title": "Forecast performance", "section": "Aggregated scores", - "text": "Aggregated scores\nAverage skill scores of each model across all sites.\n\nScores are shown by reference date and forecast horizon (in days).\n\nScores are averaged across all submissions of the model with a given horizon or a given reference_datetime using submissions made since 2023-03-06.\n\nLearn about the continous ranked probablity score here\n\nAquatics: chrophyll-a" + "text": "Aggregated scores\nAverage skill scores of each model across all sites.\n\nScores are shown by reference date and forecast horizon (in days).\n\nScores are averaged across all submissions of the model with a given horizon or a given reference_datetime using submissions made since 2023-03-07.\n\nLearn about the continous ranked probablity score here\n\nAquatics: chrophyll-a" }, { "objectID": "learn-more.html", @@ -60,7 +60,7 @@ "href": "index.html#why-a-forecasting-challenge", "title": "EFI-USGS River Chlorophyll Forecasting Challenge", "section": "Why a forecasting challenge?", - "text": "Why a forecasting challenge?\nOur vision is to use forecasts to advance theory and to support natural resource management. We can begin to realize this vision by creating and analyzing a catalog of forecasts from a range of ecological systems, spatiotemporal scales, and environmental gradients.\nOur forecasting challenge is platform for the ecological and data science communities to advance skills in forecasting ecological systems and for generating forecasts that contribute to a synthetic understanding of patterns of environmental predictability. Rewards for contributing are skill advancement, joy, and potential involved in manuscripts. We do not currently crown winner nor offer financial awards.\nThe original NEON forecasting challenge has been an excellent focal project in university courses and this EFI-USGS challenge could be used as classroom projects as well.\n \n\n\n\n\n\n\n\n\n\nTotal forecasts submitted to the EFI-USGS Challenge\n77\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost recent data for model training\n2024-03-05\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of years of data for model training\n15.13\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of variables being forecasted\n1" + "text": "Why a forecasting challenge?\nOur vision is to use forecasts to advance theory and to support natural resource management. We can begin to realize this vision by creating and analyzing a catalog of forecasts from a range of ecological systems, spatiotemporal scales, and environmental gradients.\nOur forecasting challenge is platform for the ecological and data science communities to advance skills in forecasting ecological systems and for generating forecasts that contribute to a synthetic understanding of patterns of environmental predictability. Rewards for contributing are skill advancement, joy, and potential involved in manuscripts. We do not currently crown winner nor offer financial awards.\nThe original NEON forecasting challenge has been an excellent focal project in university courses and this EFI-USGS challenge could be used as classroom projects as well.\n \n\n\n\n\n\n\n\n\n\nTotal forecasts submitted to the EFI-USGS Challenge\n80\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost recent data for model training\n2024-03-06\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of years of data for model training\n15.13\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNumber of variables being forecasted\n1" }, { "objectID": "index.html#the-challenge-is-a-platform", @@ -81,7 +81,7 @@ "href": "index.html#acknowledgements", "title": "EFI-USGS River Chlorophyll Forecasting Challenge", "section": "Acknowledgements", - "text": "Acknowledgements\nThomas, R. Q., Boettiger, C., Carey, C. C., Dietze, M. C., Johnson, L. R., Kenney, M. A., et al. (2023). The NEON Ecological Forecasting Challenge. Frontiers in Ecology and the Environment, 21(3), 112–113. https://doi.org/10.1002/fee.2616 We thank the EFI community for feedback on the design of the Challenge. This material is based upon work supported by the National Science Foundation under Grant DEB-1926388. Page last updated on 2024-03-05" + "text": "Acknowledgements\nThomas, R. Q., Boettiger, C., Carey, C. C., Dietze, M. C., Johnson, L. R., Kenney, M. A., et al. (2023). The NEON Ecological Forecasting Challenge. Frontiers in Ecology and the Environment, 21(3), 112–113. https://doi.org/10.1002/fee.2616 We thank the EFI community for feedback on the design of the Challenge. This material is based upon work supported by the National Science Foundation under Grant DEB-1926388. Page last updated on 2024-03-06" }, { "objectID": "instructions.html", diff --git a/targets.html b/targets.html index 2d64a739e0..d017d8928f 100644 --- a/targets.html +++ b/targets.html @@ -386,8 +386,8 @@

Aquatics

Explore the sites

-
- +
+



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.

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