Distribution shift, which represents how far the OOD test set deviates from the training set, can be evaluated using two complementary approaches: model-agnostic and modeldependent methods. In model-agnostic approaches, the distance between the OOD test and training sets is quantified without training ML models (as detailed in section Materials and Methods). The greater this distance, the harder the splitting strategy is considered to be.