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scenario_id
int64
oxygen_demand
float64
respiratory_buffer
float64
intervention_delay
float64
organ_coupling
float64
ventilation_stress
float64
drift_gradient
float64
drift_velocity
float64
drift_acceleration
float64
boundary_distance
float64
perturbation_radius
float64
collapse_trigger
int64
label_respiratory_cascade
int64
1
0.79
0.31
0.58
0.55
0.63
0.67
0.56
0.3
0.08
0.06
1
1
2
0.65
0.48
0.4
0.44
0.42
0.25
0.32
0.09
0.25
0.19
0
0
3
0.92
0.17
0.73
0.71
0.78
0.84
0.72
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
0.95
0.13
0.83
0.81
0.85
0.92
0.8
0.57
0.02
0.01
1
1
6
0.69
0.42
0.46
0.49
0.47
0.36
0.39
0.14
0.18
0.13
0
0
7
0.77
0.33
0.59
0.56
0.64
0.59
0.5
0.24
0.11
0.07
1
1
8
0.54
0.63
0.27
0.34
0.38
-0.03
0.18
-0.02
0.43
0.37
0
0
9
0.85
0.23
0.68
0.67
0.72
0.73
0.62
0.36
0.07
0.05
1
1
10
0.61
0.52
0.36
0.41
0.43
0.2
0.28
0.07
0.29
0.22
0
0

What this repo does

This dataset models respiratory 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 respiratory system is so close to collapse that even small perturbations trigger systemic deterioration.

Core cascade nodes

oxygen_demand
respiratory_buffer
intervention_delay
organ_coupling
ventilation_stress

These nodes represent interacting components of respiratory physiology.

oxygen_demand captures rising metabolic and ventilatory burden.

respiratory_buffer represents remaining pulmonary reserve and adaptive capacity.

intervention_delay reflects delayed oxygen escalation, ventilation adjustment, or corrective treatment.

organ_coupling represents propagation of dysfunction across organ systems.

ventilation_stress represents mechanical and physiological strain on the respiratory system.

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_respiratory_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

oxygen_demand
respiratory_buffer
intervention_delay
organ_coupling
ventilation_stress

drift_gradient
drift_velocity
drift_acceleration
boundary_distance

perturbation_radius
collapse_trigger

label_respiratory_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:

team@clarusinvariant.com

Instability is detectable.
Governance determines whether it propagates.

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