ClarusC64/f1-quad-tyre-age-track-temp-fuel-load-driving-style-performance-drop-v0.1

What this repo does

This dataset models nonlinear tyre degradation collapse in Formula One. It predicts when combined stress from tyre age, track temperature, fuel load, and driving aggression triggers a late-stint performance drop.

Core quad

tyre_age_laps track_temp_c fuel_load_kg driving_style_index

Prediction target

label_performance_drop

Binary forward label predicting whether tyre performance collapse occurs within the next stint window.

Row structure

Each row represents a race-state snapshot during a stint. The model evaluates whether the interaction between wear, heat, weight, and driving intensity produces imminent performance 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.