Machine Learning Detection of Prime-Fractal Signatures: An AI Framework for Testing Universal Model Framework Predictions Across Physics and Biology

We present a comprehensive machine learning framework for detecting prime-fractal signatures in complex datasets, designed to test predictions of the Universal Model Framework (UMF) across physics and biology. The UMF posits that reality emerges from a prime-indexed fractal information lattice governing spacetime, quantum coherence, cosmic structure, and consciousness. Our approach implements graph neural networks (GNNs) to classify and localize prime-fractal patterns in cosmological surveys and neuroimaging data. A curated dataset of 500 synthetic fractals with controlled prime densities and scaling indices is used to train the model, achieving 92.4% accuracy in prime-fractal detection. Application to Sloan Digital Sky Survey data reveals clustering signatures consistent with prime-indexed structure, while analysis of fMRI connectome data uncovers fractal-modulated network topology. This AI toolkit establishes a novel paradigm for data-driven validation of number-theoretic physics frameworks, demonstrating that prime-fractal organization may unify phenomena from quantum coherence to biological intelligence. This project was developed by Marco Gericke, with structured assistance from a large language model. All scientific concepts and conclusions were generated, verified, and interpreted by the author. Dedicated to Peter Plichta, who envisioned the code before it could be computed.