Admissibility-Gated Structural Load as a Streaming Sensor for Regime Commitment

I introduce a model-free, domain-agnostic structural sensor for sequential data based on two coupled quantities: admissibility, which functions as a structural gate, and selection load, a scalar cost accumulated under admissible stress. The method operates deterministically on batch or streaming data and requires no labels, training, or domain-specific modeling. Admissibility isolates transformations that preserve structural integrity, while selection load quantifies irreversible regime commitment within that admissible region. Canonical synthetic benchmarks establish null symmetries and quantitative sensitivity, including block permutation and rank-mixing transformations that produce no signal. A boundary illustration using timestamp-order violation demonstrates admissibility gating in action. A streaming application to real seismograph waveform data reveals discrete structural commitment steps and precursor tails that emerge prior to macroscopic events, without invoking prediction or domain-specific interpretation. The results establish admissibility-gated selection load as a general-purpose structural sensing operator suitable for real-time diagnostics across physical, biological, and informational systems.