Update DeepForest.qmd

Author: qualiaMachine

Toolbox/Models/DeepForest.qmd

@@ -80,9 +80,9 @@ This makes it easy to adapt DeepForest to new regions, forest types, or species
 
 ## Performance expectations
 
-- Tree crown model: F1-scores between 0.73 and 0.95 depending on site and canopy structure ([Weinstein et al. (2019), Remote Sensing, DOI: 10.3390/rs11111309](https://www.mdpi.com/2072-4292/11/11/1309))
-- Bird model: ~65% recall on new data; ~84% recall with just 1,000 local annotations  ([Weinstein et al. (2022), Ecological Applications, DOI: 10.1002/eap.2694](https://doi.org/10.1002/eap.2694))
-- Alive/dead classifier: 95.8% accuracy on held-out image crops ([Hugginf Face model card](https://huggingface.co/weecology/cropmodel-deadtrees)) 
+- Tree crown model: F1-scores between 0.73 and 0.95 depending on site and canopy structure [[Weinstein et al. (2019), Remote Sensing, DOI: 10.3390/rs11111309](https://www.mdpi.com/2072-4292/11/11/1309)]
+- Bird model: ~65% recall on new data; ~84% recall with just 1,000 local annotations  [[Weinstein et al. (2022), Ecological Applications, DOI: 10.1002/eap.2694](https://doi.org/10.1002/eap.2694)]
+- Alive/dead classifier: 95.8% accuracy on held-out image crops [[Hugging Face model card](https://huggingface.co/weecology/cropmodel-deadtrees)]
 
 Performance varies by imagery quality, tree species, and geographic region. Fine-tuning is recommended for most new applications.