UNNS Laboratory v0.9.2
Reliability & Structural Diagnostics Layer
The first τ-Field Laboratory capable of evaluating not only molecular spectra,
but its own predictions — introducing the Quality Geometry layer.
1. Why v0.9.2 Is Not “Just an Update”
Previous versions of the UNNS Laboratory performed increasingly sophisticated matching between τ-field curvature microstructure and real molecular spectra. Yet all evaluations were descriptive: they measured how well the projection aligned with experiment.
Version v0.9.2 adds a fundamentally new layer: the ability for the Lab to evaluate itself.
- It can distinguish whether χ² inflation comes from noisy data or structural mismatch.
- It can detect curvature–residual dependencies (κ).
- It can score the reliability of each manifold independently.
- It can predict outliers before they occur.
These capabilities transform the Lab from a matching pipeline into a field-theoretic diagnostic system — the first step toward a calibration-ready UNNS framework.
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2. The New Quality Geometry Layer
The core innovation of v0.9.2 is a set of structural metrics that analyze the τ-field fit beyond simple χ² scoring.
2.1 Structural vs Experimental χ²
v0.9.2 separates two previously conflated quantities:
- χ²norm — How well the τ-field explains the structure.
- χ²σ-weighted — How precise the dataset is.
2.2 Curvature–Residual Coherence (κ)
κ measures how strongly τ-curvature predicts residual deviation. High κ indicates underfitting or structural conflict.
2.3 Manifold Reliability R
Each manifold receives a reliability score:
R = exp( − κ · (χ²norm / 20) )
2.4 Unified τ-Reliability τR
The global reliability of the experiment is the mean reliability of all manifolds:
τR = mean(Rmanifold)
2.5 Expected Outliers (ΣP)
For each residual Δfi we compute:
Pi = 1 − exp( −(|Δfi| / 20)² )
Summing all Pi yields an expected outlier count — a statistical forecast of anomalies.
3. Why v0.9.2 Matters for the UNNS Substrate
These new metrics have major consequences:
- First cross-molecule comparability via a unified τ-Reliability scale.
- Ability to diagnose structural mismatches independent of data quality.
- A foundation for future τ-calibration and hyperfine-field extraction.
- Preparation for the v1.0 milestone.
In short: v0.9.2 makes the Laboratory scientifically aware of itself.
4. Relation to v0.9.1
UNNS Lab v0.9.2 preserves:
- All matching logic from v0.6.0.
- The v0.9.1 nonlinear τ-projection polynomial.
- The v0.9 manifold hyperfine solver.
- The UI and data ingestion pipeline.
No mechanics were changed — only extended. All quality metrics are additive and non-intrusive.
5. Availability
The v0.9.2 Laboratory interface is provided as:
- unns_lab_v0_9_2.html (Research Preview)
- Compatible with existing v0.9.x Real Data packs
- Includes new file: quality_v092.js
6. Conclusion
UNNS Laboratory v0.9.2 is the first version capable of evaluating not only molecular spectra, but its own τ-field predictions. With structural adequacy, dataset precision, curvature coherence, manifold reliability, τ-reliability, and statistical outlier forecasting, v0.9.2 introduces a full Quality Geometry Layer that prepares the UNNS Substrate for cross-molecule calibration and the approach to v1.0.