degradation-robustness

This arXiv paper studies the problem of factor-derived proxy supervision in scientific machine learning, using RUSLE-based soil-loss prediction as a case study. It introduces a diagnostic framework and proposes RASPL, a formula-preserving residual learning framework that outperforms direct prediction and improves robustness to degraded factor information.