To better understand the Gaussian process regression (GPR) models that were trained on the GBE data, we also interrogated their correlation lengths (as opposed to the input data only). This was done by the semivariogram method, with the GBEs (E (x i ) and E (x j ) in Eq. ( )) given by the GPR model (which is the mean of a collection of posterior models). This was done in two ways: (1) by evaluating the posterior models at the same locations as the input data, and (2) by evaluating the posterior models at randomly selected points (replications of which facilitate uncertainty quantification). The correlation lengths obtained by all of these methods are summarized in Table .