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We then assessed the ability of four in silico tools to predict the class of theCACNA1Fmissense variants (Table1). The performance of these tools was highly variable, with a notable variation in the false positive rate (FPR). However, when using the unscaled/raw scores of CADD, at a thr...
The multinomial logistic regression models showed that physical and environmental variables were the main drivers of either remaining in the current land-cover class or transitioning to another class (Fig.6, TableS3). Forest was most likely to remain as forest (94% probability) and its persistence...