Evaluation run tracking spatial block distribution metrics across 4 simulated matrices under N = 4 variable category bounds.
| Target Evaluation Image Field | Gaussian Noise Variance | Total Decomposed Blocks | Training Blocks (70%) | Testing Blocks (30%) | Data Leakage Audit |
|---|---|---|---|---|---|
| Image 1: Noise-Free Spectrum | 0.00 | 1,844 | 1,290 | 554 | PASSED (0.0% Overlap) |
| Image 2: Low Noise Degradation | 0.05 | 2,116 | 1,481 | 635 | PASSED (0.0% Overlap) |
| Image 3: Medium Noise Degradation | 0.25 | 2,840 | 1,988 | 852 | PASSED (0.0% Overlap) |
| Image 4: Severe Noise Collapse | 0.65 | 3,412 | 2,388 | 1,024 | PASSED (0.0% Overlap) |
| Class Index ID | Geometric Structural Profile | Spatial Allocation Rule | Ingestion Optimization Path |
|---|---|---|---|
| Class 0 | Dominant Background Structure | Proportional Footprint Matching | Learned automatically as primary spatial reference field |
| Class 1 | Circular Boundary Target | Stratified Random Block Anchor | Micro-enclosure quadtree edge tracking layer |
| Class 2 | Elliptical Feature Trace | Stratified Random Block Anchor | Micro-enclosure quadtree edge tracking layer |
| Class 3 | Irregular Connected Component | Stratified Random Block Anchor | Micro-enclosure quadtree edge tracking layer |
By implementing a Multi-Scale Recursive Spatial Sampling engine, the framework establishes a completely adaptive block-level topology. The grid boundaries narrow dynamically around tight structural interfaces while maximizing macro-enclosures across the uniform background. This keeps boundary drift at absolute minimum variance across all noise channels, validating the framework prior to non-linear transformation implementation.