ESCT v9.6: A Multi-Scale Interpretive Framework for Consciousness Emergence — Unifying Microscale Bistability, Mesoscale Frequency Filtering, and Macroscale Competitive Ignition Through Noise-Critical Distillation Testing

We present ESCT v9.6, an interpretive framework that organizes three previously independent formulations of the Entropy Selection Consciousness Theory (ESCT) into a coherent multi-scale structure. Using a brute-force distillation test measuring noise_critical — the noise threshold at which each formulation loses its activated state — across 36 parameter conditions, we demonstrate that v9.5 (microscale bistable dynamics), v9.4 (mesoscale frequency-domain filtering), and v8.1 (macroscale four-layer competitive ignition) are not equivalent projections of a common parent manifold. Instead, they describe consciousness emergence at three distinct spatiotemporal scales, with noise sensitivities differing by 4-10x: macroscale (0.052), mesoscale (0.123), and microscale (>0.60). This structured difference is consistent with known experimental findings: Layer-5 pyramidal neuron bistability (Suzuki & Larkum 2020), cortical oscillatory frequency selection (Purdon 2013), and whole-brain integration collapse (Casali 2013) operate at different noise tolerances. Three falsifiable predictions are derived: during anesthesia induction, macroscale global integration collapses first (T₃), followed by mesoscale oscillatory synchrony (T₂), with microscale neuronal bistability being the most robust (T₁), giving T₃ < T₂ < T₁. No new mathematical equations, free parameters, or open problems are introduced. v9.6 represents an interpretive-layer contribution — demonstrating that significant organizational value can be derived from the structured comparison of existing mathematical formulations without new construction. Qualia ∉ Domain(C_hat) — The Hard Problem is not solved; it is precisely bounded. consciousness emergence, multi-scale framework, bistability, frequency filtering, competitive ignition, anesthesia temporal sequence, Layer-5 pyramidal neurons, noise threshold, ESCT This paper makes no new empirical claims and introduces no new mathematics; its contribution is organizational — it shows that three existing ESCT formulations describe the same phenomenon at different scales, and derives three predictions that did not previously exist in the literature.