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README.md

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----
-license: mit
----
+---
+language: en
+license: mit
+task_categories:
+- text-classification
+tags:
+- clinical-trials
+- five-node-cascade
+size_categories:
+- 1K<n<10K
+pretty_name: Clarus Clinical Five Node Shock Cascade Boundary v0.2
+---
+
+# What this repo does
+
+This dataset evaluates whether machine learning models can detect shock 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
+
+shock_pressure  
+physiological_buffer  
+intervention_delay  
+organ_coupling  
+hemodynamic_reserve
+
+These five interacting variables represent a richer shock-state model 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 physiological trajectory is strongly aligned toward the shock 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_shock_boundary_transition
+
+The label identifies whether the system is undergoing a shock 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
+
+Shock boundary progression is continuous in clinical reality.
+
+For benchmarking purposes the outcome is simplified to binary classification:
+
+0 stable regime  
+1 shock boundary transition
+
+This makes the benchmark suitable for evaluating early warning performance.
+
+# Row structure
+
+scenario_id  
+shock_pressure  
+physiological_buffer  
+intervention_delay  
+organ_coupling  
+hemodynamic_reserve  
+drift_gradient  
+label_shock_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.