---
language:
- en
license: mit
task_categories:
- text-classification
tags:
- clarus
- f1
- stint-collapse
- latent-instability
- cross-coupling
- pre-failure
size_categories:
- 1K<n<10K
pretty_name: F1 Latent Cross-Coupling Stint Collapse Dynamics v0.1
---

# What this repo does

This dataset detects hidden instability in F1 stint performance before visible collapse occurs.

It identifies when degradation acceleration, pace-management fragility, and track-evolution pressure are interacting in a way that will produce stint collapse dynamics.

# Core structure

This dataset models:

- latent instability across a stint
- degradation acceleration under load
- pace-management fragility
- cross-coupled stint collapse risk

# Prediction target

Binary:

- `1` → stint collapse likely due to hidden instability plus interacting pressures
- `0` → instability remains contained or below meaningful activation threshold

Target column:

- `label_stint_collapse_dynamics`

# Row structure

Each row represents an F1 stint state.

Columns:

- observable_state
- latent_instability_score
- cross_coupling_intensity
- hidden_state_index
- activation_threshold_distance
- degradation_acceleration
- pace_management_fragility
- track_evolution_pressure
- stabilization_buffer

# Column meaning

### degradation_acceleration
How quickly the tyre and pace profile are worsening across the stint.

### pace_management_fragility
How unstable the current pace-management approach has become.

### track_evolution_pressure
How strongly changing track conditions are amplifying instability.

### key dynamic

Collapse occurs when:

- degradation begins accelerating
- pace management becomes fragile
- track evolution increases pressure
- stabilization buffer cannot compensate

# Label logic

`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 degradation_acceleration >= 0.68 AND pace_management_fragility >= 0.68 AND track_evolution_pressure >= 0.70 AND track_evolution_pressure > stabilization_buffer else 0`

# Files

- `data/train.csv`
- `data/tester.csv`
- `scorer.py`
- `README.md`

# 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

```bash
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 race analysis detects stint collapse after it has already become visible through lap-time drop-off, tyre fall-off, or strategic vulnerability.

This dataset class targets hidden instability before overt stint collapse becomes active in race dynamics.

That makes it useful for:

live stint-risk monitoring

pit-window timing

pace-management review

degradation acceleration detection

race collapse prevention

License

MIT

Structural Note

This dataset belongs to the Clarus latent detection layer.

It is designed to detect instability during formation, before overt stint collapse becomes active in race behavior.

Production Deployment

Applicable to:

F1 teams

race strategy groups

simulation workflows

motorsport analytics systems

live performance monitoring

Enterprise and Research Collaboration

Suitable for:

teams

analytics companies

simulation groups

motorsport R&D

race engineering workflows


Label check

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