The GP-Normal method uses temperature scaling to adjust the variance of a normal distribution. Unlike standard temperature scaling, GP-Normal incorporates Gaussian process (GP) parameter estimation to dynamically determine a scaling factor for each input. This approach ensures the recalibrated output maintains a parametric Gaussian distribution and achieves variance calibration by aligning the predicted variance with the observed error variance (mean squared error). GP-Normal provides a flexible way to assign individual recalibration weights to each sample, blending variance calibration with a broader goal of distribution calibration for Gaussians. Importantly, it modifies only the predicted variance, leaving the mean unchanged.