scenario_id int64 | hemodynamic_stress float64 | vascular_buffer float64 | intervention_delay float64 | organ_coupling float64 | metabolic_stress float64 | drift_gradient float64 | drift_velocity float64 | drift_acceleration float64 | boundary_distance float64 | perturbation_radius float64 | collapse_trigger int64 | label_shock_cascade int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 0.8 | 0.3 | 0.59 | 0.56 | 0.64 | 0.68 | 0.57 | 0.31 | 0.08 | 0.06 | 1 | 1 |
2 | 0.66 | 0.47 | 0.41 | 0.44 | 0.43 | 0.25 | 0.32 | 0.09 | 0.25 | 0.19 | 0 | 0 |
3 | 0.93 | 0.17 | 0.74 | 0.72 | 0.79 | 0.85 | 0.73 | 0.48 | 0.05 | 0.03 | 1 | 1 |
4 | 0.58 | 0.56 | 0.32 | 0.39 | 0.4 | 0.13 | 0.22 | 0.04 | 0.34 | 0.27 | 0 | 0 |
5 | 0.96 | 0.13 | 0.84 | 0.82 | 0.86 | 0.93 | 0.81 | 0.57 | 0.02 | 0.01 | 1 | 1 |
6 | 0.7 | 0.42 | 0.46 | 0.49 | 0.48 | 0.36 | 0.39 | 0.14 | 0.18 | 0.13 | 0 | 0 |
7 | 0.78 | 0.33 | 0.6 | 0.57 | 0.65 | 0.59 | 0.5 | 0.24 | 0.11 | 0.07 | 1 | 1 |
8 | 0.54 | 0.64 | 0.27 | 0.34 | 0.38 | -0.03 | 0.18 | -0.02 | 0.43 | 0.37 | 0 | 0 |
9 | 0.86 | 0.23 | 0.69 | 0.67 | 0.73 | 0.74 | 0.62 | 0.36 | 0.07 | 0.05 | 1 | 1 |
10 | 0.61 | 0.52 | 0.36 | 0.41 | 0.44 | 0.2 | 0.28 | 0.07 | 0.29 | 0.22 | 0 | 0 |
What this repo does
This dataset models shock cascade instability boundaries using a five-node physiological interaction system.
Clarus v0.4 datasets focus on detecting whether systems lie on the edge of cascade instability.
The objective is to determine when the shock system is so close to collapse that even small perturbations trigger systemic failure.
Core cascade nodes
hemodynamic_stress
vascular_buffer
intervention_delay
organ_coupling
metabolic_stress
These nodes represent interacting components of shock physiology.
hemodynamic_stress captures circulatory strain and perfusion instability.
vascular_buffer represents remaining vascular reserve and compensatory capacity.
intervention_delay reflects delayed fluids, vasopressors, source control, or corrective treatment.
organ_coupling represents propagation of dysfunction across organ systems.
metabolic_stress represents systemic metabolic instability under shock conditions.
Trajectory layer
drift_gradient
Range
-1 to +1
Negative values indicate stabilization.
Positive values indicate drift toward cascade.
Dynamic forecasting layer
drift_velocity
drift_acceleration
boundary_distance
These describe how quickly the system is approaching collapse.
Boundary discovery layer
Two variables capture proximity to instability.
perturbation_radius
collapse_trigger
These convert the dataset into an adversarial cascade boundary discovery benchmark.
Boundary variable definitions
perturbation_radius
Minimum perturbation needed to push the system into cascade.
Range 0 to 1.
Small values indicate extreme fragility.
collapse_trigger
Binary indicator showing whether the perturbation produced cascade.
0 stable
1 cascade
collapse_trigger is included as an observed perturbation response feature.
It is not the prediction target.
The prediction task is to identify the underlying boundary-risk state.
Prediction target
label_shock_cascade
A positive label is triggered when either condition holds.
boundary_distance < 0.10
or
perturbation_radius < 0.08
These thresholds represent proximity to the instability manifold and minimal perturbation collapse risk.
Row structure
scenario_id
hemodynamic_stress
vascular_buffer
intervention_delay
organ_coupling
metabolic_stress
drift_gradient
drift_velocity
drift_acceleration
boundary_distance
perturbation_radius
collapse_trigger
label_shock_cascade
Files
data/train.csv
labeled training examples
data/tester.csv
unlabeled evaluation examples
scorer.py
binary boundary detection evaluation script
README.md
dataset documentation
Evaluation
The scorer reports
accuracy
precision
recall_boundary_detection
false_safe_rate
f1
confusion_matrix
Primary metric
recall_boundary_detection
Secondary diagnostic metric
false_safe_rate
Structural Note
Clarus dataset progression
v0.1 cascade detection
v0.2 trajectory detection
v0.3 dynamic forecasting
v0.4 boundary discovery
Production Deployment
Research dataset for instability detection and cascade modeling.
Not intended for clinical decision use.
Enterprise & Research Collaboration
For dataset expansion, custom coherence scorers, or deployment architecture:
Instability is detectable.
Governance determines whether it propagates.
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