---
language: en
license: mit
task_categories:
  - text-classification
tags:
  - clinical-trials
  - trajectory-aware
  - clarus
  - latent-cross-coupling
  - oxygen
  - buffer
size_categories:
  - n<1K
pretty_name: Clinical Latent Cross Coupling Oxygen Buffer Instability v0.2
---

# Clinical Latent Cross Coupling Oxygen Buffer Instability v0.2

## What this is

A small dataset that tests one question:

Can you detect when an oxygen-buffer system is moving toward hidden instability, not just carrying visible strain?

This repo focuses on latent cross coupling between oxygen delivery and buffer capacity.

It models a system where:

- oxygen delivery may weaken
- buffer capacity may erode
- latent coupling pressure may rise
- compensation fatigue may accumulate before overt collapse 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

oxygen-buffer instability 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 oxygen-buffer collapse

Data

Each row represents a latent oxygen-buffer coupling state.

Core variables:

oxygen_delivery_score

buffer_capacity_index

latent_coupling_pressure

compensation_fatigue_score

drift_gradient

coherence_stability_score

context_integrity_score

decision_readiness_score

Target:

label_oxygen_buffer_instability

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 latent coupling classification

v0.2:

adds direction via drift_gradient

This allows you to separate:

strained but stabilizing coupling states

strained and deteriorating coupling states

Why this exists

Most models answer:

what is happening now

This tests:

where the hidden interaction is going

That difference is where failure 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 oxygen-buffer strain from active deterioration in latent cross coupling.

Production Deployment

This dataset can be used in:

respiratory instability research

compensation failure monitoring

hidden coupling benchmarking

critical care trajectory modeling

model benchmarking for trajectory-aware latent coupling 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.