Coverage‐Aware Calibration and Selective Classification for Drifted Gas‐Sensor Arrays: A Controlled Shift Simulation Benchmark With Longitudinal Evaluation

Electronic‐nose screening models degrade after deployment because sensor drift and environmental variability change both separability and confidence calibration, yet field datasets rarely allow controlled severity analysis. We study calibration‐aware selective classification on a synthetic 16‐channel MOX benchmark that parameterizes humidity, temperature, aging, device variability, and noise to create mild, moderate, and severe target domains, and includes calibration anchors. The proposed CAN‐OCS pipeline combines anchor normalization, nuisance suppression, CORAL covariance alignment using unlabeled target batches, and an uncertainty gate that defers low‐confidence samples to confirmatory testing while reporting coverage. At matched coverage, CAN‐OCS outperformed a standardized SVM baseline with severity‐dependent gains (Holm‐adjusted p = 0.0146 in the severe domain; borderline in moderate; not significant in mild). On a public one‐year drift dataset (24 temporal splits), fixed thresholding caused strong coverage contraction (median 0.439). Results emphasize that accepted‐sample accuracy must be interpreted jointly with coverage and anchor validity.