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Discussion (6 Comments)Read Original on HackerNews
> We then used these observations to train a machine learning model. The model was built using dozens of geospatial environmental layers, including climate, vegetation, soils, and land use. By learning how measured hyphal density varied across these environmental conditions, the model allowed us to predict the density of AM fungal networks across Earth’s terrestrial ecosystems at a fine spatial resolution of approximately 1 km². We also used spatial uncertainty analyses, including bootstrapping, to understand where predictions were more or less certain.
This is absolutely mindblowing work! One of the best things I've ever seen on the internet. I only wish it was granular enough to help identify mother trees[0] in individual forests
[0] https://www.scientificamerican.com/article/mother-trees-are-...
Imagine that.