Update README.md

README.md

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----
-license: mit
----
+---
+language: en
+license: mit
+task_categories:
+  - text-classification
+tags:
+  - clinical-trials
+  - trajectory-aware
+  - clarus
+  - diagnostic-logic
+  - fragility
+  - atlas
+size_categories:
+  - n<1K
+pretty_name: Clinical Diagnostic Logic Fragility Atlas v0.2
+---
+
+# Clinical Diagnostic Logic Fragility Atlas v0.2
+
+## What this is
+
+A small dataset that tests one question:
+
+Can you detect when diagnostic logic is moving toward fragility, not just carrying ambiguity?
+
+This repo focuses on diagnostic logic breakdown under clinical reasoning pressure.
+
+It models a system where:
+
+- diagnostic signal consistency may weaken
+- hypothesis conflict may rise
+- evidence may fragment
+- inference stability may erode before overt decision failure appears
+
+## Run this first
+
+Generate baseline predictions:
+
+```bash
+python baseline_heuristic.py data/tester.csv predictions.csv
+
+Score them:
+
+python scorer.py data/tester.csv predictions.csv
+
+That is enough to see the full evaluation loop.
+
+You will get:
+
+standard metrics
+
+trajectory detection performance
+
+diagnostic fragility detection errors
+
+What to try next
+
+Replace the baseline.
+
+Build your own model.
+
+Output a file like:
+
+id,prediction_score
+0,0.12
+1,0.81
+2,0.67
+
+Then run:
+
+python scorer.py data/tester.csv your_predictions.csv
+What matters
+
+Not just accuracy.
+
+The key signals are:
+
+recall_trajectory_deterioration_detection
+
+false_stable_trajectory_rate
+
+These tell you:
+
+are you catching systems that are getting worse
+
+are you missing hidden diagnostic collapse
+
+Data
+
+Each row represents a diagnostic reasoning state.
+
+Core variables:
+
+diagnostic_signal_consistency
+
+hypothesis_conflict_index
+
+evidence_fragmentation_score
+
+inference_stability_score
+
+drift_gradient
+
+logic_gap_pressure
+
+context_loss_index
+
+decision_coherence_score
+
+Target:
+
+label_diagnostic_logic_fragility
+
+Important distinction
+
+There are two different components in this repo.
+
+scorer.py
+
+evaluates predictions
+
+domain-agnostic
+
+works across all v0.2 datasets
+
+does not generate predictions
+
+baseline_heuristic.py
+
+generates predictions
+
+domain-specific
+
+uses the variables in this dataset
+
+Do not reuse the heuristic across datasets.
+
+It is only a local reference.
+
+What changed from v0.1
+
+v0.1:
+
+static diagnostic fragility classification
+
+v0.2:
+
+adds direction via drift_gradient
+
+This allows you to separate:
+
+strained but stabilizing reasoning systems
+
+strained and deteriorating reasoning systems
+
+Why this exists
+
+Most models answer:
+
+what is happening now
+
+This tests:
+
+where the reasoning structure is going
+
+That difference is where fragility appears early.
+
+Files
+
+data/train.csv — training data
+
+data/tester.csv — evaluation data
+
+scorer.py — canonical evaluation script
+
+baseline_heuristic.py — dataset-specific reference model
+
+README.md — dataset card
+
+Evaluation
+
+Primary metric:
+
+recall_trajectory_deterioration_detection
+
+Secondary metric:
+
+false_stable_trajectory_rate
+
+Standard metrics are also reported:
+
+accuracy
+
+precision
+
+recall
+
+f1
+
+The scorer supports binary predictions or score-based predictions.
+
+License
+
+MIT
+
+Structural Note
+
+Clarus datasets are structural instruments.
+
+They are designed to expose instability geometry, not just predict isolated outcomes.
+
+This v0.2 repo adds directional state movement so the dataset can separate static diagnostic strain from active deterioration in reasoning coherence.
+
+Production Deployment
+
+This dataset can be used in:
+
+diagnostic reasoning research
+
+differential conflict monitoring
+
+clinical synthesis benchmarking
+
+decision support stress testing
+
+model benchmarking for trajectory-aware diagnostic logic reasoning
+
+It is suitable for research and prototyping.
+
+It is not a substitute for live clinical judgment.
+
+Enterprise & Research Collaboration
+
+Clarus builds datasets for:
+
+instability detection
+
+trajectory tracking
+
+intervention reasoning
+
+These structures are not domain-bound.
+
+They apply wherever systems move toward or away from failure.
+
+Applicable domains include:
+
+healthcare systems
+
+financial markets
+
+energy infrastructure
+
+logistics networks
+
+artificial intelligence systems
+
+manufacturing systems
+
+supply chains
+
+climate systems
+
+Any environment where:
+
+capacity and demand interact
+
+delays and coupling exist
+
+trajectory determines outcome
+
+This dataset is one instance of a general stability framework.