In many cases, the uncertainty quantification tools that we have discussed here are used in concert with uncertainty calibration techniques. Various calibration methods exist for adjusting the magnitude of uncertainty predictions in the context of regression models. Application of these methods often work by scaling the uncertainty predictions made by a model to match the real errors observed in a held out calibration dataset. Application of calibration methods may serve to improve some uncertainty evaluation metrics, such as miscalibration area, while still providing uncertainty quantifications with functional shortcomings. Two useful evaluation metrics to consider for the suitability of uncertainty quantification calibrations are sharpness and dispersion, as discussed in the context of materials datasets by Ref. 95. Sharpness refers to the average level of precision predicted by a model, in that a model that is accurately represented as low uncertainty is better than a model that is accurately represented as high uncertainty. Dispersion refers to the ability of a model to distinguish between high and low uncertainty predictions within a dataset. Failures to account for error types using the appropriate tools may be compensated for with calibration techniques, but doing so inappropriately will often lead to poor sharpness and dispersion. In the systematic noise section of this paper, the failure of ensemble variance to distinguish between error regimes is an example of poor dispersion, even when scaled to a calibrated level. We caution the reader to apply calibration methods carefully and check their validity using multiple evaluation metrics.