Keywords
structural
Abstract
This review examines error analysis for multitarget structural and decoding models through an evidence-centered design lens. The analysis asks how local errors should guide model revision. It treats the relevant unit as a complete pathway from data or physical observations to representation, model output, human interpretation, and accountable action. The cited literature is synthesized without inventing experiments or unreported performance values. Particular attention is given to aggregate metrics concealing consequential failure modes. The review argues that credible translation requires explicit evidence boundaries, uncertainty-aware evaluation, author-visible traceability, and a documented route for intervention. The resulting framework supports structural biology and neural decoding while distinguishing component promise from system readiness.
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