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etiology_divergence_score float64 | cross_basin_pressure_index float64 | escape_route_viability float64 | transition_lock_in_score float64 | drift_gradient float64 | recovery_competition_score float64 | latent_failure_load float64 | coordination_stability_score float64 | label_cross_basin_escape_failure int64 |
|---|---|---|---|---|---|---|---|---|
0.18 | 0.24 | 0.82 | 0.21 | -0.17 | 0.78 | 0.23 | 0.88 | 0 |
0.27 | 0.35 | 0.71 | 0.33 | -0.09 | 0.66 | 0.34 | 0.77 | 0 |
0.43 | 0.49 | 0.57 | 0.48 | 0.06 | 0.53 | 0.48 | 0.62 | 1 |
0.56 | 0.61 | 0.46 | 0.59 | 0.15 | 0.41 | 0.61 | 0.5 | 1 |
0.68 | 0.73 | 0.34 | 0.71 | 0.24 | 0.3 | 0.74 | 0.41 | 1 |
0.22 | 0.28 | 0.79 | 0.26 | -0.14 | 0.73 | 0.27 | 0.84 | 0 |
0.75 | 0.81 | 0.27 | 0.79 | 0.31 | 0.22 | 0.82 | 0.34 | 1 |
0.37 | 0.42 | 0.63 | 0.39 | 0.01 | 0.59 | 0.41 | 0.69 | 0 |
0.59 | 0.66 | 0.39 | 0.64 | 0.18 | 0.36 | 0.68 | 0.46 | 1 |
0.16 | 0.21 | 0.86 | 0.18 | -0.2 | 0.81 | 0.2 | 0.9 | 0 |
Clinical Etiological Cross Basin Transition Escape Analysis v0.2
What this is
A small dataset that tests one question:
Can you detect when a clinical system is moving toward cross-basin escape failure, not just carrying etiological instability?
This repo focuses on etiological cross-basin transition and escape analysis.
It models a system where:
- etiology divergence may widen
- cross-basin pressure may rise
- escape route viability may shrink
- transition lock-in may trap the system in a worsening basin
Run this first
Generate baseline predictions:
python baseline_heuristic.py data/tester.csv predictions.csv
Score them:
python scorer.py data/tester.csv predictions.csv
That is enough to see the full evaluation loop.
You will get:
standard metrics
trajectory detection performance
cross-basin escape failure detection errors
What to try next
Replace the baseline.
Build your own model.
Output a file like:
id,prediction_score
0,0.12
1,0.81
2,0.67
Then run:
python scorer.py data/tester.csv your_predictions.csv
What matters
Not just accuracy.
The key signals are:
recall_trajectory_deterioration_detection
false_stable_trajectory_rate
These tell you:
are you catching systems that are getting worse
are you missing hidden transition failure
Data
Each row represents an etiological transition state.
Core variables:
etiology_divergence_score
cross_basin_pressure_index
escape_route_viability
transition_lock_in_score
drift_gradient
recovery_competition_score
latent_failure_load
coordination_stability_score
Target:
label_cross_basin_escape_failure
Important distinction
There are two different components in this repo.
scorer.py
evaluates predictions
domain-agnostic
works across all v0.2 datasets
does not generate predictions
baseline_heuristic.py
generates predictions
domain-specific
uses the variables in this dataset
Do not reuse the heuristic across datasets.
It is only a local reference.
What changed from v0.1
v0.1:
static cross-basin transition classification
v0.2:
adds direction via drift_gradient
This allows you to separate:
unstable but recoverable transition states
unstable and deteriorating transition states
Why this exists
Most models answer:
what is happening now
This tests:
where the system is going
That difference is where escape failure appears early.
Files
data/train.csv — training data
data/tester.csv — evaluation data
scorer.py — canonical evaluation script
baseline_heuristic.py — dataset-specific reference model
README.md — dataset card
Evaluation
Primary metric:
recall_trajectory_deterioration_detection
Secondary metric:
false_stable_trajectory_rate
Standard metrics are also reported:
accuracy
precision
recall
f1
The scorer supports binary predictions or score-based predictions.
License
MIT
Structural Note
Clarus datasets are structural instruments.
They are designed to expose instability geometry, not just predict isolated outcomes.
This v0.2 repo adds directional state movement so the dataset can separate static etiological transition pressure from active deterioration in cross-basin escape dynamics.
Production Deployment
This dataset can be used in:
etiological transition research
deterioration pathway analysis
recovery route mapping
intervention timing studies
model benchmarking for trajectory-aware escape reasoning
It is suitable for research and prototyping.
It is not a substitute for live clinical judgment.
Enterprise & Research Collaboration
Clarus builds datasets for:
instability detection
trajectory tracking
intervention reasoning
These structures are not domain-bound.
They apply wherever systems move toward or away from failure.
Applicable domains include:
healthcare systems
financial markets
energy infrastructure
logistics networks
artificial intelligence systems
manufacturing systems
supply chains
climate systems
Any environment where:
capacity and demand interact
delays and coupling exist
trajectory determines outcome
This dataset is one instance of a general stability framework.
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