Previous SAMPL challenges have included quantilequantile plots (QQ plots) which provide a more quantitative assessment of a participants reported model uncertainties . QQ plots are based on the concept that actual errors should be drawn from a normal distribution, and well-predicted uncertainties should be able to predict the frequency of deviations of a given size. Thus in QQ plots, y-axis has the fraction of predicted minus experimental values that fall within a given number of uncertainties and the x-axis shows the fraction of a normal distribution within that many standard deviations. The closer the predicted uncertainties compare to a normal distribution the closer they will come to an x = y line. Thus, the slope of a regression is also often used as a part of the evaluation. We compare two possibilities for model uncertainty in our QQ plot. The first uncertainty approach we consider uses the predicted uncertainties from our Gaussian process model (blue circles in Figure ). Another common way to report uncertainty is to assume it is the same for all predictions based on past performance of the approach. For the second set of data, we assumed the uncertainty for each predicted pK a was equal to the RMSE for our internal test set, 0.75 (red squares in Figure ). A method producing accurate predicted uncertainties should lead to a diagonal line on the resulting plot, with slope of 1; in this case, we find that the uncertainty model using predicted uncertainties from the Gaussian process (slope=0.87) outperforms the model with a fixed uncertainty (slope=0.73). This is promising evidence that our model is capable of predicting how its reliability varies with the chemistry being considered, rather than just its overall typical performance.