Metabolic Reserve as a Cofactor in AI Governance Safety Metrics: A 90-Day Longitudinal Self-Study

Current AI governance frameworks assess human-AI collaboration health through behavioral interaction metrics alone. The Human Symbiosis Score (HSS 2.0), a six-domain composite measuring Intent Clarity, Constraint Awareness, Collaboration Discipline, Cognitive Load Regulation, Interaction Stability, and Outcome Reliability, gates AI autonomy levels based on observed operator performance. This paper presents a 90-day longitudinal self-study documenting the construction of a 15,458-line AI governance framework, a 220-instrument generative audio ecosystem, and a 2.47-million-entry knowledge engine by a single operator working from a bare-metal GPU cluster in Edmonton, Alberta (November 2025 -- March 2026). Retrospective application of HSS 2.0 and the proposed HSS 3.0 (which incorporates Metabolic Reserve as a multiplicative physiological substrate) reveals a critical divergence: HSS 2.0 behavioral scores remained in the Guided-to-Competent range (0.35--0.50) throughout the highest-output phases, while reconstructed HSS 3.0 scores fell to 0.06--0.13 --- Critical --- due to severe physiological depletion. The data demonstrate that behavioral metrics systematically mask substrate collapse during sustained high-output periods, and that a multiplicative physiological cofactor is necessary to prevent governance systems from misclassifying depleted operators as functional. Two injury events occurring at reconstructed MR values below 0.20 provide incidental clinical evidence that executive function degradation --- specifically, the omission of safety precautions in manual tasks --- correlates with substrate depletion in a pattern consistent with established prefrontal function literature.