ClarusC64/f1-quad-tyre-temp-brake-temp-pack-compression-reaction-delta-restart-position-loss-v0.1

What this repo does

This dataset models restart instability in Formula One. It predicts when the interaction between tyre temperature readiness, brake temperature readiness, pack compression intensity, and reaction delay creates a high probability of losing positions at a safety car restart.

Core quad

tyretempindex braketempindex packcompressionindex reactiontimedelta_s

Prediction target

labelrestartposition_loss

Binary forward label predicting position loss across the restart phase due to thermal readiness and pack dynamics.

Row structure

Each row represents a restart setup snapshot in the final moments before the green flag. The model evaluates whether low thermal readiness and high pack compression amplify small reaction delays into position loss.

Files

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

Evaluation

Run predictions on tester.csv Add column prediction 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.