THE SEMANTIC TOLERANCE LAW Statistical Law PART II_ THE EMPIRICAL EVIDENCE

We present comprehensive empirical validation of the Semantic Tolerance Law across 13,018 classification datasets (5,885 OpenML + 7,133 Kaggle), drawn from a comprehensive scan of 54,370 machine learning datasets. Φ predicts structural collapse with 98.4% success on curated data (OpenML) and 66.1% adjusted success on wild data (Kaggle)—representing the largest pre-training quality assessment in machine learning history. Key Results: Datasets Validated: 13,018 classification tasks Datasets Scanned: 54,370 total (41,352 filtered as regression/invalid) OpenML (Curated): 98.4% success (N=5,885) Kaggle (Wild): 66.1% adjusted success (N=7,133) Threshold: Φ_c ≈ 0.007 (empirical, range 0.003-0.012) Known Limitations: 3 edge cases (