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The paper introduces a domain-adapted retrieval-augmented generation framework for reliable mechanistic reasoning in corrosion science, using fine-tuned language models to improve prediction and explainability, demonstrated on magnesium alloy data.
An arXiv paper presents an interpretable machine-learning framework for predicting the splitting strength of asphalt concrete using SHAP analysis, comparing six models with TabPFN performing best.