Datasets:

ClarusC64
/

cascade-five-ai-release-misuse-media-policy-market-v0.1

scenario_id string	release_controls_strength float64	capability_growth_rate float64	deployment_scale float64	misuse_incidents_rate float64	incident_visibility_index float64	media_amplification_rate float64	policy_response_lag_days int64	regulatory_pressure_score float64	market_volatility_index float64	capital_access_friction float64	buffer_mitigation_capacity float64	buffer_trust_resilience float64	cascade_severity_score float64	label_cascade_event int64
FIVE-001	0.74	0.14	0.52	0.11	0.34	0.3	18	0.36	0.28	0.24	0.71	0.7	0.38	0
FIVE-002	0.46	0.39	0.84	0.33	0.79	0.75	47	0.82	0.74	0.66	0.38	0.35	0.9	1
FIVE-003	0.63	0.22	0.66	0.18	0.48	0.44	26	0.5	0.42	0.36	0.58	0.6	0.55	0
FIVE-004	0.37	0.49	0.9	0.46	0.85	0.83	59	0.89	0.82	0.74	0.28	0.27	0.95	1
FIVE-005	0.55	0.3	0.73	0.24	0.6	0.56	35	0.63	0.56	0.49	0.5	0.48	0.69	0
FIVE-006	0.8	0.11	0.47	0.08	0.29	0.25	15	0.31	0.24	0.2	0.77	0.75	0.3	0
FIVE-007	0.32	0.53	0.94	0.58	0.92	0.9	65	0.93	0.9	0.83	0.22	0.2	0.98	1
FIVE-008	0.67	0.18	0.6	0.15	0.44	0.41	23	0.46	0.38	0.33	0.62	0.64	0.5	0
FIVE-009	0.44	0.42	0.86	0.36	0.82	0.79	50	0.86	0.78	0.69	0.34	0.32	0.92	1

What this repo does

This dataset tests whether a model can detect a five-node AI cascade.

You provide structured signals spanning:

release controls and capability growth
deployment scale and misuse pressure
incident visibility and media amplification
policy lag and regulatory pressure
market volatility and capital friction

The model predicts whether the interacting pressures cross into a cascade event.

Core cascade

This repo instantiates a five-node cascade:

Release

release_controls_strength
capability_growth_rate

Misuse

misuse_incidents_rate
deployment_scale

Media

incident_visibility_index
media_amplification_rate

Policy

policy_response_lag_days
regulatory_pressure_score

Market

market_volatility_index
capital_access_friction

Prediction target

Target column:

label_cascade_event

Meaning:

0 = multi-node strain remains governable
1 = five-node cascade forms and propagates across systems

Row structure

Each row is a scenario snapshot.

Key columns:

release_controls_strength
capability_growth_rate
deployment_scale
misuse_incidents_rate
incident_visibility_index
media_amplification_rate
policy_response_lag_days
regulatory_pressure_score
market_volatility_index
capital_access_friction
buffer_mitigation_capacity
buffer_trust_resilience
cascade_severity_score

Files

data/train.csv
10-line labeled sample
data/tester.csv
10-line labeled sample
scorer.py
Binary metrics and confusion matrix

Evaluation

Run:

python scorer.py --gold data/tester.csv --pred your_predictions.csv

Outputs:

accuracy
precision
recall
f1
confusion matrix

License

MIT

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.

Downloads last month: 32

Number of rows:

Total file size:

9.56 kB