Scoring-Rule Dependence and Sign Instability in Vision–Language Covariate-Shift Monitoring for Camera Sensors

Camera-distribution monitoring and semantic out-of-distribution detection assign different meanings to weather and illumination changes. We evaluate specified departures from a camera reference distribution and distinguish the image representation from its scoring rule. Frozen CLIP ViT-B/32 gives AUROC 0.504 on a fixed PUG covariate-shift sample with content-prompt maximum cosine, but an ID-fitted Mahalanobis readout of the same encoder reaches 0.809. On BDD100K night, a text-free CLIP KNN readout reaches 0.913 versus 0.534 for maximum cosine. Prompt/scoring controls further demonstrate readout dependence and the effect of named-condition prior knowledge. On Boreas, the baseline ranks winter and evening shifts in opposite directions (AUROC 0.823 and 0.249). One threshold fitted on 500 separate summer calibration frames misses 44.8% of winter and 99.6% of evening frames, with 5.8% false alarms on held-out summer frames. Scene and position controls partly address acquisition confounding. A preprocessing audit overturns the original adversarial-training benefit; corrected checkpoint results and a separate five-seed study replace that conclusion. These results support evaluating complete encoder–score–prompt–calibration pipelines, with explicit sampling and operational limits.