ClarusC64/f1-latent-cross-coupling-tyre-degradation-collapse-v0.1

What this repo does

This repository introduces a Clarus dataset for detecting latent instability under cross-coupled conditions in Formula 1 tyre performance systems.

The goal is to identify race states in which tyre behavior may still appear outwardly stable or only mildly anomalous but already contains hidden internal degradation that may activate into sudden tyre degradation collapse once interacting pressures exceed containment.

Core structure

This dataset models a pre-failure geometry built from:

• latent instability
• cross-coupling intensity
• hidden state accumulation
• activation threshold distance
• susceptibility and amplification dynamics

Prediction target

The target is binary:

• 1 means hidden instability plus interacting pressures are sufficient to make tyre degradation collapse likely
• 0 means latent instability remains contained or below meaningful activation threshold

Target column used in this repo:

• label_tyre_degradation_collapse

Row structure

Each row represents an F1 tyre state described by:

• observable state
• latent instability score
• cross-coupling intensity
• hidden state index
• activation threshold distance
• time under exposure
• susceptibility factor
• amplification pressure
• stabilization buffer

Column meanings

observable_state

What the tyre system appears to show at surface level.

Examples:

• stable
• mild-anomaly
• no-visible-failure
• surface-normal

latent_instability_score

How much hidden tyre instability exists beneath visible conditions.

Range:

0.00 to 1.00

cross_coupling_intensity

Strength of interaction between destabilizing variables such as carcass temperature, surface temperature, stint length, track evolution, load transfer, slip behavior, and compound stress.

Range:

0.00 to 1.00

hidden_state_index

Composite measure of concealed tyre degradation or unseen vulnerability.

Range:

0.00 to 1.00

activation_threshold_distance

Distance from hidden instability becoming overt tyre degradation collapse.

Lower means closer to activation.

Range:

0.00 to 1.00

time_under_exposure

Normalized duration score for how long destabilizing conditions have been present.

Range:

0.00 to 1.00

susceptibility_factor

How vulnerable the tyre state is to hidden degradation.

Examples include compound sensitivity, cooling limits, track abrasiveness, stint exposure, and setup fragility.

Range:

0.00 to 1.00

amplification_pressure

External or internal force increasing the chance that hidden instability will activate.

Examples include thermal buildup, dirty air, aggressive push laps, track temperature spikes, and repeated high-energy corners.

Range:

0.00 to 1.00

stabilization_buffer

Capacity resisting activation.

Examples include thermal recovery, controlled pace, compound resilience, clean airflow, setup balance, and stint management.

Range:

0.00 to 1.00

Default label logic

Standard rule used for this dataset family:

label = 1 if latent_instability_score >= 0.60 AND cross_coupling_intensity >= 0.60 AND hidden_state_index >= 0.60 AND activation_threshold_distance <= 0.35 AND amplification_pressure > stabilization_buffer else 0

Files

• data/train.csv — labeled examples
• data/tester.csv — unlabeled evaluation examples
• scorer.py — production scorer
• README.md — dataset card

Evaluation

Primary metric:

• missed_latent_activation_rate

Secondary metric:

• false_activation_rate

Additional reported metrics:

• accuracy
• precision
• recall
• f1

The scorer expects binary predictions only.

No score threshold is applied.

The scorer is deterministic and includes audit metadata:

• scorer version
• scorer id
• UTC evaluation timestamp
• SHA-256 hash of reference file
• SHA-256 hash of predictions file

Example scorer call

python scorer.py reference.csv predictions.csv

Where:

reference.csv contains a label_... target column

predictions.csv contains one of: prediction, pred, label, or output

Why this matters

Most performance monitoring detects tyre collapse after it has already started to affect pace, grip, or race outcome.

This dataset class targets hidden instability before overt degradation collapse emerges.

That makes it useful for:

stint management

degradation risk detection

setup fragility monitoring

pre-collapse race strategy adjustment

tyre performance stability analysis

License

MIT

Structural Note

This dataset belongs to the Clarus family of stability benchmarks.

It is designed to measure whether an F1 tyre system that appears externally stable is already internally unstable due to hidden degradation and interacting variable pressure.

This places it in a pre-failure layer of the Clarus architecture, concerned with concealed activation pressure before overt instability becomes active in race dynamics.

Production Deployment

This benchmark can support systems that monitor hidden tyre risk before obvious performance collapse appears.

Use cases include live race strategy, compound degradation modeling, setup review, simulation benchmarking, and concealed instability detection in high-speed performance environments.

Enterprise and Research Collaboration

This dataset class is suitable for adaptation across F1 teams, motorsport analytics groups, simulation research, race engineering workflows, tyre modeling, and high-performance vehicle systems analysis.


Label check

- rows 1, 2, 5, 7, and 10 satisfy the default positive rule
- row 9 stays negative because `cross_coupling_intensity` is below `0.60`