ClarusC64/building-latent-cross-coupling-mould-proliferation-v0.1

What this repo does

This repository introduces a Clarus dataset for detecting latent instability under cross-coupled conditions in building environments.

The goal is to identify structures that still appear outwardly stable but already contain hidden internal degradation that may activate into overt mould proliferation once interacting pressures exceed containment.

Core structure

This dataset models a pre-failure geometry built from:

• latent instability
• cross-coupling intensity
• hidden state accumulation
• activation threshold distance
• susceptibility and amplification dynamics

Prediction target

The target is binary:

• 1 means hidden instability plus interacting pressures are sufficient to make mould proliferation likely
• 0 means latent instability remains contained or below meaningful activation threshold

Target column used in this repo:

• label_mould_proliferation

Row structure

Each row represents a building state described by:

• observable state
• latent instability score
• cross-coupling intensity
• hidden state index
• activation threshold distance
• time under exposure
• susceptibility factor
• amplification pressure
• stabilization buffer

Column meanings

observable_state

What the building surface appears to show.

Examples:

• stable
• mild-anomaly
• no-visible-failure
• surface-normal

latent_instability_score

How much hidden instability exists beneath visible conditions.

Range:

0.00 to 1.00

cross_coupling_intensity

Strength of interaction between destabilizing variables such as moisture retention, trapped damp, limited ventilation, porous material breakdown, and concealed contamination.

Range:

0.00 to 1.00

hidden_state_index

Composite measure of concealed degradation or unseen vulnerability.

Range:

0.00 to 1.00

activation_threshold_distance

Distance from hidden instability becoming overt mould proliferation.

Lower means closer to activation.

Range:

0.00 to 1.00

time_under_exposure

Normalized duration score for how long destabilizing conditions have been present.

Range:

0.00 to 1.00

susceptibility_factor

How vulnerable the materials are to hidden degradation.

Examples include chipboard, saturated insulation, trapped moisture zones, and poor drying conditions.

Range:

0.00 to 1.00

amplification_pressure

External or internal force increasing the chance that hidden instability will activate.

Examples include repeated damp events, cold bridging, low airflow, water ingress history, and persistent humidity load.

Range:

0.00 to 1.00

stabilization_buffer

Capacity resisting activation.

Examples include drying capacity, remediation quality, airflow recovery, thermal stability, and removal of degraded materials.

Range:

0.00 to 1.00

Default label logic

Standard rule used for this dataset family:

label = 1 if latent_instability_score >= 0.60 AND cross_coupling_intensity >= 0.60 AND hidden_state_index >= 0.60 AND activation_threshold_distance <= 0.35 AND amplification_pressure > stabilization_buffer else 0

Files

• data/train.csv — labeled examples
• data/tester.csv — unlabeled evaluation examples
• scorer.py — production scorer
• README.md — dataset card

Evaluation

Primary metric:

• missed_latent_activation_rate

Secondary metric:

• false_activation_rate

Additional reported metrics:

• accuracy
• precision
• recall
• f1

The scorer expects binary predictions only.

No score threshold is applied.

Example scorer call

python scorer.py reference.csv predictions.csv

Where:

reference.csv contains a label_... target column

predictions.csv contains one of: prediction, pred, label, or output

Why this matters

Most benchmark datasets detect visible failure.

This dataset class targets hidden instability before overt mould proliferation emerges.

That makes it useful for:

early warning

concealed moisture risk tracking

pre-remediation triage

hidden degradation monitoring

building safety review

License

MIT

Structural Note

This dataset belongs to the Clarus family of stability benchmarks.

It is designed to measure whether a building that appears externally stable is already internally unstable due to hidden degradation and interacting variable pressure.

This places it in a pre-failure layer of the Clarus architecture, concerned with concealed activation pressure before overt instability becomes visible.

Production Deployment

This benchmark can support systems that monitor hidden building risk before obvious material failure appears.

Use cases include early warning, survey support, remediation prioritization, concealed hazard detection, and mould activation monitoring.

Enterprise and Research Collaboration

This dataset class is suitable for adaptation across housing, surveying, insurance, remediation, facilities management, environmental health, and building risk research.


One operational note

- `data/tester.csv` is unlabeled by design
- so public evaluation requires either a hidden reference file or a separate labeled evaluation file held outside the public repo

Next repo in the series should be:

- `ClarusC64/clinical-latent-cross-coupling-sepsis-onset-v0.1`