Delin–Vermes (DV) Statistical Detection Framework: Physics-Based Decision Criteria for Detection in Chaotic Molecular Environments

This record presents the Delin–Vermes (DV) Statistical Detection Framework, a physics-based decision methodology for assessing detection feasibility in chaotic molecular environments (e.g., gas sensing, breath analysis, environmental monitoring). The framework introduces a scalar detectability index (D_DV) derived from classical statistical detection theory (Neyman–Pearson / MAP), explicitly accounting for environmental variability, turbulence, drift, and sensor noise. The objective of this work is not to introduce new fundamental detection theory, but to provide a decision-grade, software-first framework enabling R&D teams to assess feasibility before committing to expensive hardware development. This release corresponds to Version 4.0, which represents the authoritative scientific and strategic formulation of the framework. Experimental validation of the framework’s predictive capability is planned as Phase 2A.