GPT OSI

A continuous normalized-coprime-factor margin estimate with authority-aware quiet-band inference and a two-second response envelope.

Study data loading Acceptance loading Target margin 0.55 Fabby score loading Estimator cadence 0.125 s Warm-up 2.0 s

Three equations, two measured terms

The coherent and quiet-band terms answer different observability questions. The first measures the loop where the command excites it. The second fits the output-spectrum shape where the command has little leverage. Weak command activity blends the result to the finite NCF ceiling, preventing displacement feedthrough from becoming false loop evidence.

01 · measured NCF
T̂ = Syu/Suu
bST = minω 1 / √[2(|1−T̂|²+|T̂|²)]

A 0.5 s sliding DFT is exponentially averaged. Coherence and input power continuously shrink uncertain bins toward the current-authority nominal model; there is no valid/invalid gate.

02 · unexcited band
q = (Syy/|S0|²) / median(Syy/|S0|²)
ρ̂ = arg minρ median |log q − log qρ|
bR = minω chordal(aρ̂L0)

A scalar loop-gain multiplier is fitted from spectral shape, so unknown noise intensity cancels. Coherent driven-band gain continuously weights this fit: a healthy low-band record stays at ρ = 1, while real damage releases the hazard-band estimate.

03 · authority
bref = bt + g(1+wR)
d ln a / dt = k · deadzone(b−bref)

One log-integral law handles shedding and recovery. The same observability weight scales its guard and deadband; a 4× rolling prediction horizon accelerates recovery without a deep-shed extrapolation to full authority. There are no latches, counters, cooldowns, or separate recovery mode.

NCF cap: this study retains FABLE’s imposed reporting cap of 1/√2 for direct comparison. It is a software convention, not the intrinsic maximum of the chordal expression, which can reach 1.

Fresh ten-case comparison

Choose a canonical case or the experimental GDBqFpKb replay. Scroll over either plot to zoom in time, drag to pan, and double-click to reset. Both panels share the same time window.

Selecting data…
NCF margin
Authority factor
true NCF / diagnostic-gain inverse (context) FABLE GPT GPT coherent term GPT quiet-band model

Acceptance results

RequirementMeasuredLimitResult

Canonical case scorecard

CaseFABLEGPTGPT warm-upGPT responseGPT recovery

What remains tunable

The algorithm exposes structural limits and four meaningful performance values. Frequency bands and the 1/√2 comparison cap describe the experiment; they are not behavioral gates.

ValueRoleSelected
segment_sRecent-data span and frequency resolution.0.50 s
memory_sExponential spectral memory; determines change detection speed.0.35 s
margin_sFinal continuous margin smoothing.0.20 s
residual_sMemory of the quiet-band gain fit.1.50 s
warm_sFinite nominal-prior warm-up.2.00 s
target / guardRequired NCF level and base observability standoff.0.55 / 0.020
deadband / rateBase NCF noise band and log-authority gain.0.008 / 5.0

How GDBqFpKb is used

Results(:,21) is a diagnostic loop-gain multiplier, not an authority command. GPT OSI never receives it; the replay uses only u_osi, dm, and the logged authority that actually acted on the plant. The replay uses the same canonical 0.55 target and default authority law as the ten-case suite.

Because this is an open-loop replay, the plotted diagnostic-gain inverse is context only—not a true authority or acceptance reference. Acceptance checks continuous output, idle behavior before the earthquake, deficit detection at earthquake onset and both destabilizing edges, then post-quiet recovery. Logged FABLE traces remain context only.