The lower RMSE for the model with 150k training samples is also apparent in the NLL which decreases (both observed and simulated) compared to the N train = 10k model. Since for each of the two models (N train = 10k and N train = 150k) we have two sets of predicted uncertainties; LS-NN i and LS-GCN i , this is a case where the error distribution is constant and differences in NLL between LS-NN i and LS-GCN i can be ascribed to performance of the uncertainties. For the N train = 10k model there is no difference in the NLL between LS-NN and LS-GCN uncertainties but the LS-NN 150k uncertainties show a better model fit than the uncertainties from LS-GCN 150k (NLL of -0.50 vs. -0.46, Table ). This is in line with the conclusions drawn from the error-based calibration plots but opposite of a conclusion drawn solely based on which has a higher ρ rank . The agreement between the observed and simulated NLL is decent and there is no clear trend that the observed NLL is either higher or lower than the simulated NLL. Thus, no further conclusions can be made from the NLL test.