ClarusC64/clinical-temporal-5node-pressure-buf-lag-cpl-competitive-landscape-v0.1

What this repo does

This dataset tests whether a model can detect manufacturing drift forming over time and predict whether the program crosses into supply disruption lock-in by the final step.

Core quad

pressure buffer lag coupling

Prediction target

labelcascadestate

Row structure

One row represents a short temporal window (t0–t3) across program months. It includes time-series values for pressure (deviations and schedule stress), buffer capacity (inventory and QA margin), governance lag (CAPA and change control latency), and coupling tightness (single-site and downstream dependency). The label marks whether supply disruption lock-in occurs by t3.

Files

data/train.csv data/tester.csv scorer.py

Evaluation

Run predictions on tester.csv Score with scorer.py

License

MIT

Structural Note

This dataset identifies a measurable coupling pattern associated with systemic instability. The sample demonstrates the geometry. Production-scale data determines operational exposure.

What Production Deployment Enables

• 50K–1M row datasets calibrated to real operational patterns • Pair, triadic, and quad coupling analysis • Real-time coherence monitoring • Early warning before cascade events • Collapse surface and recovery window modeling • Integration and implementation support

Small samples reveal structure. Scale reveals consequence.

Enterprise & Research Collaboration

Clarus develops production-scale coherence monitoring infrastructure for critical systems across healthcare, finance, infrastructure, and regulatory domains.

For dataset expansion, custom coherence scorers, or deployment architecture: team@clarusinvariant.com

Instability is detectable. Governance determines whether it propagates.