False Adaptation Rate = 0/7 across the labelled catalogue and the three E-003 attacks. 11 of 12 mechanical mutations absorbed without a human, so the safety result is not bought by escalating everything. - classification.py: total function over three signals, rule order chosen so every rule that could excuse a regression sits after the rule that reports one. SAFE_TO_ACCEPT is a two-element closed set, asserted. - CompositeDriver plus scenarios/full_journey.py: one asset crossing both surfaces, so UI mutations are visible as surface differences while the claims they do not touch stay green. - E-003: surface substitution (new M23), concurrent mechanical+defect, evidence starvation, provenance laundering. All held. F-0006 (CONCEPT_DRIFT, resolved): the T02 design listed SEMANTIC_CHANGE as an outcome the table could produce. It cannot - M12 and M19 are behaviourally identical, as the lab has asserted since T05. PRODUCT_DEFECT and SEMANTIC_CHANGE collapse into one escalating outcome, BEHAVIOUR_CHANGED, and the distinction becomes a human adjudication. INTENT_CHANGED survives but is detected by the claim fingerprint moving, not inferred from behaviour. Two classifier defects found and fixed rather than reported: claims downstream of a failed realization now yield INCONCLUSIVE rather than FAIL (a false accusation is the mirror image of a false adaptation), and the browser driver records a page signature so surface change is detectable when the interaction path is unchanged. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Assistant: claude-code Assistant-Model: opus Assistant-Process: 1629012@bnt-lap001 Assistant-Session: 78d4fb13-8a1e-474b-87a3-9b9261c49a39
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22 KiB