We report our findings organized into three key areas: (1) the analysis of the distribution shift and distance between the OOD test sets and training sets using both model-agnostic and model-dependent approaches, (2) the evaluation of the performance gap between ID and OOD test sets across different splitting methods and model architecture types, and (3) the investigation of the correlation between ID and OOD performance.