ECLIPSE v2.0: A Systematic Falsification Framework with Quantitative Integrity Metrics

The replication crisis reveals pervasive methodological weaknesses, with replication failure rates exceeding 60% in psychology and preclinical biology. While preregistration improves transparency, it lacks technical enforcement and quantitative assessment of rigor. ECLIPSE v2.0 introduces a computational framework that enforces falsification integrity through cryptographic data splitting, single-shot validation, and automated auditing.The framework integrates five sequential stages: (1) irreversible data partitioning with SHA-256 verification; (2) binding preregistration of falsification criteria; (3) cross-validated model development; (4) single-shot holdout testing; and (5) automatic integrity assessment. Three quantitative tools were developed: the Eclipse Integrity Score (EIS) measuring methodological rigor; the Statistical Test for Data Snooping (STDS) detecting performance inflation; and an Automated Code Auditor using multi-level static analysis to identify protocol violations.Across four validation studies, ECLIPSE achieved: (1) strong discrimination between honest and p-hacked studies (Cohen’s d = 3.54, AUC = 0.991); (2) 89% sensitivity for detecting 15% performance inflation; (3) auditor accuracy F1 = 0.908; and (4) prediction of replication success in 73 published studies (ρ = 0.68), outperforming impact factor and sample size.ECLIPSE provides the first quantitative, enforceable, and scalable methodology for ensuring research integrity through prevention, detection, and assessment. Open-source implementation is available at https://github.com/camilosjobergtala/AFH-MODEL.