As with our demonstrations of behavior under random noise (Fig. , Fig. ), we use the dataset of noisefree additive enthalpies to demonstrate behavior under systematic noise. We use two different cases of systematic noise application as demonstrations, using training dataset sizes of 711,613 datapoints. In the first case (Fig. , violet), we apply Gaussian noise of standard deviation 20 kcal/mol for Nitrogen-containing molecules and Gaussian noise of standard deviation 2 kcal/mol for non-Nitrogen-containing molecules. When training a model to predict these datapoints using a mean-variance estimation approach, the model is able to distinguish between the noise regimes of the non-Nitrogen-containing molecules and the Nitrogen-containing molecules. For non-Nitrogen-containing molecules in the test set, the model has a RMSE of 2.12 kcal/mol and a mean predicted standard deviation of 2.35 kcal/mol. For the Nitrogen-containing molecules in the test set, the model has a RMSE of 20.0 kcal/mol and a mean predicted standard deviation of 20.0 kcal/mol. We see that in this case where the noise is the predominant error source and clearly delineated based off the input features to the model, the mean-variance estimation method performs well at quantifying the error magnitude in the different noise regimes.