scenario_id int64 | oxygen_demand float64 | physiological_buffer float64 | intervention_delay float64 | organ_coupling float64 | respiratory_reserve float64 | drift_gradient float64 | label_respiratory_boundary_transition int64 |
|---|---|---|---|---|---|---|---|
1 | 0.62 | 0.68 | 0.17 | 0.22 | 0.72 | 0.48 | 0 |
2 | 0.78 | 0.56 | 0.28 | 0.33 | 0.41 | 0.74 | 1 |
3 | 0.54 | 0.76 | 0.12 | 0.18 | 0.79 | -0.46 | 0 |
4 | 0.85 | 0.47 | 0.36 | 0.42 | 0.37 | 0.82 | 1 |
5 | 0.59 | 0.71 | 0.16 | 0.23 | 0.75 | -0.28 | 0 |
6 | 0.8 | 0.52 | 0.3 | 0.37 | 0.39 | 0.68 | 1 |
7 | 0.51 | 0.78 | 0.1 | 0.16 | 0.82 | -0.6 | 0 |
8 | 0.88 | 0.44 | 0.39 | 0.45 | 0.34 | 0.86 | 1 |
9 | 0.64 | 0.66 | 0.18 | 0.25 | 0.69 | -0.21 | 0 |
10 | 0.82 | 0.5 | 0.33 | 0.39 | 0.4 | 0.75 | 1 |
What this repo does
This dataset evaluates whether machine learning models can detect respiratory cascade boundary transitions using both system state and system trajectory.
Earlier Clarus datasets in the v0.1 series tested whether models could classify system state alone.
Clarus v0.2 datasets add a trajectory signal so models must determine not only where the system is, but where it is moving inside state space.
This benchmark therefore tests whether models can read trajectory inside state space and detect approaching instability even when the current state still appears locally stable.
Five-node core
oxygen_demand
physiological_buffer
intervention_delay
organ_coupling
respiratory_reserve
These five interacting variables represent a richer respiratory state for boundary-sensitive collapse detection.
All variables are normalized between 0 and 1.
Together they define the current position of the system inside the stability manifold.
Trajectory Layer
This dataset includes a trajectory variable called drift_gradient.
drift_gradient measures the directional alignment between the system’s motion vector and the instability gradient of the system’s potential field.
Positive values indicate motion toward cascade.
Negative values indicate motion toward recovery.
This converts the dataset from a static boundary classifier into a trajectory-aware cascade boundary detection benchmark.
The core test is whether models can read trajectory inside state space rather than relying only on static variable levels.
Drift definition
Let the system state be represented by vector x(t).
The previous system state is x(t−1).
System motion
Δx = x(t) − x(t−1)
Instability gradient
g = ∇Φ(x)
Drift alignment
drift_gradient = (Δx · g) / (||Δx|| ||g||)
Interpretation
+1 system moving directly toward cascade
0 neutral movement
−1 system moving toward recovery
Operational meaning
A positive drift_gradient such as +0.65 means the respiratory trajectory is strongly aligned toward the instability boundary even if the current state appears only moderately stressed.
This is the false stability detection problem encoded by v0.2 datasets.
Prediction target
label_respiratory_boundary_transition
The label identifies whether the system is undergoing a respiratory cascade boundary transition.
High drift toward cascade can trigger a positive label even when state variables remain within nominal range.
This lets the dataset test whether models can identify instability from state plus trajectory, not state alone.
Binary simplification note
Respiratory boundary progression is continuous in clinical reality.
For benchmarking purposes the outcome is simplified to binary classification:
0 stable regime
1 respiratory boundary transition
This makes the benchmark suitable for evaluating early warning performance.
Row structure
scenario_id
oxygen_demand
physiological_buffer
intervention_delay
organ_coupling
respiratory_reserve
drift_gradient
label_respiratory_boundary_transition
State variables range between 0 and 1.
drift_gradient ranges between -1 and +1.
Files
data/train.csv
data/tester.csv
scorer.py
README.md
Evaluation
Binary classification metrics
accuracy
precision
recall_cascade_detection
false_safe_rate
f1
confusion_matrix
Primary metric
recall_cascade_detection
This prioritizes whether a model can detect cascade onset before visible deterioration.
false_safe_rate tracks the proportion of true cascade cases incorrectly predicted as safe.
License
MIT
Structural Note
Clarus datasets explore instability dynamics in coupled systems.
The v0.2 series adds trajectory awareness to test whether models can detect movement toward failure boundaries rather than only classify static state snapshots.
Production Deployment
These datasets support experimentation and benchmarking for early instability detection across domains including:
clinical deterioration
infrastructure instability
financial contagion
AI multi-agent systems
supply chain collapse
Enterprise & Research Collaboration
Organizations interested in trajectory-aware cascade detection, false stability benchmarking, or operational early warning systems may contact the Clarus project for collaboration.
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