refine conclusions

index.qmd

@@ -288,7 +288,7 @@ Global Elevation.                                            contribution     0.
 ![AUC](ROC.png)
 
 # Conclusions
-My project utilized environmental variables to predict the potential global distribution of *Takakia*. The results indicated that Annual Mean Temperature, Precipitation of the Driest Month, and Mean Diurnal Range were the most influential environmental variables affecting the distribution of *Takakia*. However, during the experiment, I noticed that the AUC values of the model's predictions were not ideal. Despite trying various methods to improve the AUC, the results fluctuated between 0.501 and 0.520 without significant improvement. So far, I am still exploring ways to enhance the model's predictive accuracy. I suspect that two factors might be limiting the model's performance:1，The resolution of environmental variables. 2，The correlations among environmental variables. In the future, I will continue working on addressing these two issues and strive to improve the model's accuracy.
+My project utilized environmental variables to predict the potential global distribution of *Takakia*. The results indicated that Annual Mean Temperature, Precipitation of the Driest Month, and Mean Diurnal Range were the most influential environmental variables affecting the distribution of *Takakia*. However, during the experiment, I noticed that the AUC values of the model's predictions were not ideal. Despite trying various methods to improve the AUC, the results fluctuated between 0.501 and 0.520 without significant improvement. So far, I am still exploring ways to enhance the model's predictive accuracy. I suspect that two factors might be limiting the model's performance: 1, The resolution of environmental variables. 2, The correlations among environmental variables. In the future, I will continue working on addressing these two issues and strive to improve the model's accuracy.