Data Provenance and Reproducibility for Photo-Fenton Hydrogen and Remediation Systems: Failure Modes across the Evidence Chain

Abstract

This critical review examines data provenance and reproducibility in photo-fenton hydrogen and remediation systems, with links to intelligent manufacturing and evidence governance. The organizing question is what provenance is necessary to reproduce a result across data, material batches, models, and operating environments. Ten or more scholarly and user-supplied records are synthesized through a mechanism-to-decision framework spanning system boundaries, measurement, representation, evaluation, translation, and governance. No experiment, participant dataset, effect estimate, or production result is invented. Particular attention is given to a reproducible computation being mistaken for a reproducible scientific conclusion. The review argues that credible translation requires source-level traceability, explicit validity domains, failure-aware evaluation, and revalidation triggers. Google Scholar-supplied records are retained in structured APA form, and no missing DOI or pagination field is completed by conjecture.