# Savant_v7 Stability Engine (RRF Φ5.2)

## Overview
The **Savant_v7 Stability Engine** is a machine-learning-based classification and prediction system designed to evaluate the topological stability of galactic rotation curves within the **Resonance of Reality Framework (Φ5.2)**. 

It utilizes a 15-dimensional physical feature schema to predict the 'Goodness of Fit' ($p_{good}$) of a 3-parameter logarithmic RRF potential: 
$$v^2(r) = GM/r + lpha \log(1 + r/r_0)$$

## Technical Specifications
- **Architecture:** Ensemble including Gradient Boosting Machine (GBM), Logistic Regression (L2), and SVM.
- **Input Schema:** 15D Feature Vector (Physical descriptors including alpha coupling, core radius, and baryonic fractions).
- **Primary Metric:** $p_{good}$ (Probability of a stable, non-chaotic topological state).
- **Theoretical Alignment:** 81.63% compliance with icosahedral unit-lattice theory.

## 15D Feature Schema
| Index | Feature | Description |
| :--- | :--- | :--- |
| 0 | log_alpha | Logarithmic RRF coupling strength |
| 1 | log_r0 | Core radius scaling parameter |
| 2 | log_M | Total galactic mass (solar units) |
| 3 | v_inf_norm | Normalized asymptotic velocity |
| 4 | chi2r_clip | Reduced Chi-Squared (Fit quality) |
| 9 | v_rms_residual | Root Mean Square error of kinematic fit |
| 11 | log_distance | Distance to galaxy (Primary stability driver) |
| 14 | keplerian_frac | Baryonic vs. RRF potential ratio at r_half |

## Key Validation Results
- **Symmetry-Breaking Threshold:** Verified at $\sigma \approx 0.000204$.
- **Global Shift Factor:** 0.0200 (Required for transitioning from kinematic to topological peaks).
- **Network Coherence:** $r \approx -0.3416$ (Observed resonance-entropy convergence).

## Repository Contents
- `rrf_v7_gbm.joblib`: Primary predictive model.
- `rrf_v7_scaler.joblib`: StandardScaler for feature normalization.
- `model_config.json`: Metadata and physical constants for deployment.

## Citation
If using this model in academic research, please cite the RRF Φ5.2 framework documentation and the SPARC dataset calibration session.

**Author:** A. Padilla Morales  
**Status:** SECURED_RESONANT | PUBLICATION_READY