Online Resource 1: Complete reproducibility archive for Interpretable Benchmarking of Machine Learning Models for Small Experimental Energy Systems

Résumé fourni par la source

Executable Online Resource and reproducibility archive accompanying the manuscript “Interpretable Benchmarking of Machine Learning Models for Small Experimental Energy Systems: Balancing Accuracy, Complexity and Physical Meaning in Biomass Gasification Prediction.” The archive contains the six direct QRO-401 analyzer records retained as empirical provenance anchors and the 98-row workbook-derived performance scenario dataset used for methodological benchmarking. The 98 scenarios are derived analytical records and must not be interpreted as 98 independent physical gasifier experiments. The repository provides the fixed analysis seed (20260901), frozen model specifications, complete repeated cross-validation generator, automated data-lineage and target-proximity detector, leave-configuration-out transport tests, full fold- and repeat-level computational results, manuscript-result verification outputs, machine-readable Online Resource tables, supplementary information, manuscript-aligned figures, pinned software environment files, repository manifest, and SHA-256 checksums. Tier A and Tier B constitute the legitimate interpolation benchmarking feature sets. Tier C includes formula-proximal energy production and is retained strictly as a leakage and formula-recovery diagnostic rather than as a deployable prediction benchmark. The archive is intended to support independent inspection, computational reproduction, provenance auditing, and verification of the results reported in the associated manuscript.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.