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Predictions in small biomass categories (< 200 Mg/ha) are comparable between the three methods, but smaller predictions > 200 Mg/ha for CRM produce 20% smaller county level estimates.
Overall, we find that RAIG makes smaller predictions that are more focused on the known target genes of the aberrations compared with GISTIC2 and GAIA.
Unlike recall and precision, these measures do not consider the number of predictions, and thus a method that fragments SCNAs into many smaller predictions can perform well on sensitivity and specificity.
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Interestingly, using information only from line B1 to predict line B2 resulted in a comparatively smaller prediction accuracy than using only information from line B2 to predict line B1.
This preactivation, in turn, may hamper the differentiation between the actual action effect and the preactivation of the predicted effect, resulting in a smaller brain activity (i.e. smaller prediction error).
By contrast, the other models provide smaller prediction intervals.
As expected, a smaller prediction leap time step t d gives more accurate prediction, and as t d increases, the error distribution flattens out.
It was noteworthy that for several months (i.e. growing seasons) ordinary kriging yielded smaller prediction errors than the linear regression of temperature against elevation and locational/topographic factors did.
However, some would be more informative than others, resulting in smaller prediction errors and less uncertainty.
We believe this to be a problem of overfitting, indicated by the fact that the larger predictor set yields the smaller prediction success in cross-validation.
Lower RMSE scores indicated a smaller prediction error.
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