A Substrate-Agnostic Measurement Standard for Coherence Drift and Rupture Precursors

The SCFL Virtual Lab is a substrate-agnostic measurement system for detecting coherence drift and rupture precursors across complex systems. It formalizes coherence as a measurable, upstream structural property and introduces a unified instrumentation layer capable of operating across physiology, cognition, infrastructure, finance, ecology, and governance domains. The system integrates heterogeneous telemetry into normalized coherence fields and computes a canonical Coherence Degradation Index (CDI), a bounded, multi-factor instrument derived from stability, phase alignment, fragmentation entropy, drift velocity, and rupture-envelope proximity. The CDI is fully specified with fixed weighting, normalization, and error bounds, enabling reproducible measurement across substrates and cadences. Validation is conducted through a four-layer falsification protocol spanning substrate, cadence, perturbation-response, and simulation-reality concordance. In a structured 27-signature universality trial, 19 candidate coherence signatures survive cross-domain testing, establishing a constrained basis for candidate invariance. Empirical demonstrations across physiological, infrastructure, and narrative datasets show identical coherence-field geometry and consistent non-linear drift dynamics preceding rupture. Two canonical validation cases—ERCOT grid instability (real-time) and the Flint water crisis (slow-time)—demonstrate cadence invariance, with identical CDI trajectories observed across systems differing by orders of magnitude in temporal scale. Head-to-head comparisons show CDI provides materially earlier detection (up to 8× in real-time systems and 5–14× in slow-time systems) while reducing false positives and false negatives relative to traditional indicators. The SCFL Virtual Lab establishes a reproducible, falsifiable measurement system for upstream failure detection, providing the foundation for coherence measurement as a cross-domain scientific discipline and a candidate standard for early-warning instrumentation.