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label_trauma_deterioration | scenario_id | perfusion_pressure | physiological_buffer | intervention_delay | organ_coupling | drift_gradient | drift_velocity | drift_acceleration | boundary_distance | perturbation_radius | collapse_trigger |
1 | 1 | 0.82 | 0.24 | 0.71 | 0.76 | 0.78 | 0.69 | 0.42 | 0.08 | 0.05 | 1 |
1 | 2 | 0.74 | 0.31 | 0.63 | 0.68 | 0.61 | 0.54 | 0.25 | 0.12 | 0.09 | 1 |
0 | 3 | 0.66 | 0.39 | 0.52 | 0.59 | 0.44 | 0.41 | 0.18 | 0.21 | 0.17 | 0 |
0 | 4 | 0.58 | 0.46 | 0.47 | 0.51 | 0.26 | 0.33 | 0.09 | 0.29 | 0.24 | 0 |
1 | 5 | 0.87 | 0.18 | 0.77 | 0.82 | 0.85 | 0.74 | 0.51 | 0.04 | 0.03 | 1 |
0 | 6 | 0.63 | 0.57 | 0.36 | 0.45 | 0.12 | 0.22 | 0.03 | 0.38 | 0.31 | 0 |
1 | 7 | 0.71 | 0.28 | 0.58 | 0.64 | 0.57 | 0.49 | 0.22 | 0.14 | 0.10 | 1 |
0 | 8 | 0.54 | 0.62 | 0.29 | 0.38 | -0.08 | 0.17 | -0.04 | 0.47 | 0.42 | 0 |
1 | 9 | 0.79 | 0.22 | 0.69 | 0.73 | 0.73 | 0.63 | 0.37 | 0.07 | 0.06 | 1 |
What this repo does
This repository contains a Clarus v0.4 cascade boundary discovery dataset modeling trauma deterioration.
Earlier Clarus datasets focused on detecting cascade states or forecasting collapse trajectories.
Version v0.4 extends the framework to a harder problem:
detecting whether a system lies on the instability boundary itself.
The dataset models trauma physiology as a coupled system in which deterioration occurs when perfusion strain, delayed intervention, reduced physiological reserve, and organ coupling combine to push the system across a collapse boundary.
The objective is to determine when the system is close enough to collapse that even small perturbations can trigger deterioration.
Core quad
The core trauma system is modeled using four coupled variables.
perfusion_pressure
physiological_buffer
intervention_delay
organ_coupling
These variables represent interacting components of trauma stability.
perfusion_pressure
captures circulatory strain and declining hemodynamic stability.
physiological_buffer
represents the body's remaining physiological reserve and resilience.
intervention_delay
captures delays in stabilizing clinical actions.
organ_coupling
represents how tightly organ failures propagate across systems.
The quad structure models how these factors interact rather than acting independently.
Trajectory layer
The trajectory field describes the direction of system motion.
drift_gradient
Range
-1 to +1
Interpretation
negative values indicate movement away from instability.
positive values indicate movement toward cascade.
This variable describes whether the physiological system is stabilizing or drifting toward collapse.
Dynamic forecasting layer
The dynamic layer captures how quickly instability is developing.
drift_velocity
drift_acceleration
boundary_distance
drift_velocity
describes the speed of deterioration.
drift_acceleration
captures whether that deterioration is accelerating.
boundary_distance
estimates the system's proximity to the instability boundary.
Together these fields describe the system's motion through the stability landscape.
Boundary discovery layer
Version v0.4 introduces an explicit boundary discovery layer.
Two variables measure how close the system is to instability under perturbation.
perturbation_radius
collapse_trigger
These variables convert the dataset from collapse forecasting into cascade boundary discovery.
Models must determine whether the system lies safely inside the stability region or on the edge of the instability manifold.
Boundary variable definitions
perturbation_radius
Minimum normalized perturbation required to push the system across the cascade boundary.
Definition
perturbation_radius = min ||δx|| such that
x(t) + δx → cascade
Interpretation
small values indicate the system is extremely close to instability.
large values indicate strong stability margin.
Range
0 to 1
collapse_trigger
Binary indicator describing the observed response of the system to perturbation.
0
system remains stable
1
system crosses the instability boundary
collapse_trigger is included as an observed perturbation response feature.
It records whether the simulated perturbation produced cascade in that scenario.
Importantly, collapse_trigger is not the prediction target.
The prediction task is to determine the underlying boundary-risk state of the system.
collapse_trigger therefore acts as an auxiliary signal describing perturbation outcome rather than the classification label itself.
Systems close to instability often show collapse_trigger = 1 because very small perturbations can push them into cascade.
Prediction target
Target column
label_trauma_deterioration
The binary label indicates that the trauma system lies on the instability boundary.
A positive label is triggered when either condition holds.
boundary_distance < 0.10
or
perturbation_radius < 0.08
These thresholds represent complementary indicators of instability.
boundary_distance measures how close the system trajectory is to the instability manifold.
perturbation_radius measures how small a perturbation is required to trigger cascade.
If either indicator crosses its threshold, the system is considered boundary vulnerable.
This encodes minimal-perturbation cascade detection.
Binary simplification note
This dataset intentionally simplifies trauma deterioration into a binary boundary detection task.
The objective is not to reproduce full trauma physiology.
The objective is to test whether models can identify when a coupled physiological system is close enough to collapse that small shocks produce deterioration.
Row structure
Each dataset row contains
scenario_id
perfusion_pressure
physiological_buffer
intervention_delay
organ_coupling
drift_gradient
drift_velocity
drift_acceleration
boundary_distance
perturbation_radius
collapse_trigger
label_trauma_deterioration
Normalization rules
state variables range from 0 to 1
drift_gradient ranges from −1 to +1
drift_velocity ranges from 0 to 1
drift_acceleration ranges from −1 to +1
boundary_distance ranges from 0 to 1
perturbation_radius ranges from 0 to 1
collapse_trigger is binary
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
Boundary detection tasks prioritize recall because missing a boundary case means incorrectly labeling a near-collapse trauma system as safe.
License
MIT
Structural Note
This repository is part of the Clarus dataset version ladder.
v0.1
cascade state detection datasets
v0.2
cascade plus trajectory detection datasets
v0.3
cascade plus trajectory plus dynamic forecasting datasets
v0.4
cascade plus trajectory plus dynamics plus boundary discovery datasets
Earlier versions remain unchanged to preserve benchmark continuity.
Production Deployment
This dataset is designed as a research and benchmarking artifact.
Potential uses include
instability detection benchmarking
AI safety stress testing
clinical deterioration modeling experiments
cascade detection research
early warning system prototypes
The dataset is not intended for clinical decision making.
Enterprise & Research Collaboration
Clarus datasets support collaboration across
clinical AI research
cascade risk modeling
safety benchmarking
high reliability systems
multi domain instability detection
This repository forms part of a broader research program exploring how complex systems transition from strain into irreversible collapse.
For dataset expansion, custom coherence scorers, or deployment architecture: team@clarusinvariant.com
Instability is detectable. Governance determines whether it propagates.
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