Evidence Genome: A Cross-Domain Framework for Classifying, Weighting, and Stress-Testing Scientific and Historical Claims v1.0

This paper introduces the Evidence Genome, a cross-domain framework for classifying, weighting, and stress-testing the evidentiary basis of scientific and historical claims. Each claim is represented as a seven-dimensional evidence vector, EG(C_i) = (D_i, R_i, I_i, X_i, U_i, T_i, P_i), representing Documentary Integrity, Reproducibility, Independent Corroboration, Inferential Distance, Uncertainty and Contestation, Predictive and Causal Evidence, and Adversarial Survival. The framework explicitly distinguishes evidence strength from truth probability and does not treat a high evidence rating as proof that a claim is true. It is designed to make evidence quality transparent, comparable, auditable, and falsifiable across domains including science, history, archaeology, artificial intelligence, computational neuroscience, and philosophy of mind. A sixteen-claim demonstration catalog is included to illustrate application of the framework. The demonstration classifications are provisional and are not treated as validation of the framework itself. Validation requires independent evaluators, inter-rater reliability testing, sensitivity analysis, weighting robustness, adversarial review, and replication. The framework establishes explicit failure conditions. If independent evaluators cannot reproduce classifications, if reasonable weighting schemes produce unstable conclusions, or if systematic domain-dependent bias is demonstrated, the framework must be revised or rejected. The Evidence Genome is intended as an evidence-classification and methodological framework rather than a truth detector. Its purpose is to make the evidentiary structure underlying difficult, controversial, emerging, and established claims more explicit and testable.