Don't Fix What Isn't Broken: Calibrated Repair of Frozen Foundation Models under Product Shift

Frozen foundation models are increasingly deployed in Earth observation, where the scene and its label stay the same but the product changes: satellites are replaced, processing baselines are updated, and resolution, radiometry, or band registration shift. We study how such product shifts break frozen pipelines and what it takes to repair them, across eight encoders, two Sentinel-2 benchmarks, 36 physically grounded degradations, and a real Level-2A-to-Level-1C product change. Only about 4% of the feature displacement reaches the readout, yet this small, concentrated component crosses decision margins and lowers micro-F1 under the real product change by 20 points on average. The information is not lost: a readout refit with labels recovers 92% of the loss. Thirteen test-time adaptation methods recover at most 29% of it, and every one that recovers accuracy also harms clean inputs; batch statistics even mistake class composition for shift, costing up to 37 points on clean streams. We introduce CALIPER, which repairs final embeddings only as far as evidence against a clean null supports, and decides for each image how far it moves. CALIPER recovers 46% of the loss while changing 7 of 15,000 clean predictions, passes a pre-registered test on unseen images, comes close to the strongest baseline on the real product change without its clean-input loss, and stays within 0.11 points on clean streams where batch-level adaptation fails.