ESCT v9.6–v9.7: A Multi-Scale Interpretive Framework for Emergence Dynamics in Nonlinear Learning Systems—From Dimensionless Theory to Calibrated Falsifiable Predictions in μV²/Hz

Background: Mathematical models of consciousness emergence have traditionally operated in dimensionless parameter spaces, preventing direct experimental testability. The Entropy-Signaling Convergence Theory (ESCT) has evolved through nine versions, achieving mathematical closure (v9.5) but remaining isolated from physical measurement.Methods: We conducted a brute-force distillation test across 36 parameter conditions to determine whether three independent ESCT formulations—v9.5 (microscale bistable dynamics), v9.4 (mesoscale frequency-domain filtering), and v8.1 (macroscale multi-layer competition)—represent equivalent projections of a common manifold or distinct scale-specific mechanisms. We resolved Open Problem 9 (dimensionless noise) by deriving a calibration formula grounded in clinical EEG literature (Niedermeyer 2005), mapping model-internal noise to physical power spectral density (μV²/Hz). Supplementary v9.7 established quantitative correspondence with published propofol-EEG signatures (Purdon et al., 2013, PNAS).Results: The three formulations are not equivalent (mean inter-formulation distance 74–134%, exceeding the 0.3% validation threshold). They describe complementary emergence mechanisms at distinct spatiotemporal scales with noise-critical values differing by 4–10× (macro: 0.052, meso: 0.123, micro: >0.60). The calibration formula (noise_model = √PSD/20.0) yields three falsifiable predictions expressed in physical units: T₃(macro collapse) < T₂(meso collapse) < T₁(micro collapse) during anesthesia induction, consistent with the known sequence of global integration loss → beta desynchronization → L5 pyramidal neuron decoupling.Conclusions: ESCT v9.6–v9.7 provide a mathematically closed, physically calibrated, and empirically addressable framework for consciousness emergence. All parameters are literature-derived; no new equations or magic numbers are introduced. The Hard Problem is not solved—it is precisely bounded. Evidence level: This contribution achieves directional consistency (Level 1) and order-of-magnitude correspondence (Level 2) with published EEG literature. Level 3 statistical validation (individual-subject trajectory analysis) awaits access to restricted EEG datasets or independent experimental collaboration. The framework is positioned as a theoretically grounded, mathematically closed, and empirically addressable contribution to consciousness science.