Responsible Automation and Governance for Bernoulli-Driven Water Separation: Failure Modes across the Evidence Chain

Abstract

This critical review examines responsible automation and governance in bernoulli-driven water separation, with links to intelligent manufacturing and evidence governance. The organizing question is how authority, documentation, and appeal should be assigned when automated evidence shapes consequential action. 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 automated recommendations becoming de facto decisions without accountable review. 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.