The miscalibration area (A mis ) addresses the average calibration of the uncertainty estimates; is the observed distribution of errors consistent with what would be expected from the predicted uncertainty distribution? The miscalibration area is found by calculating the calibration curve, which plots the observed fraction of errors vs. the expected fraction of errors. This is done assuming the predicted uncertainties to describe a Gaussian error distribution. The errors are expressed in terms of Z-scores (Z i = εi σi ) describing the error, ε i , as a fraction of its predicted uncertainty, σ i . Assuming the uncertainties are correct and the errors are Gaussian, the distribution of Z-scores should represent a Gaussian distribution with variance=1. Therefore at |Z| > 3 we expect to see a fraction of 0.003 of the errors, at |Z| > 2 we expect to see 0.045 of the errors and so on. Scanning through |Z| > x until x = 0 and plotting the observed vs. expected fraction of Z-scores results in the calibration curve (see section S1 for more details). Perfect calibration results in a diagonal line and the miscalibration area is the area between the calibration curve and the diagonal line.