JOR Framework v3.1: Organizational User Manual — Bayesian Fusion Engine with Stochastic Flight Modeling for UAP Case Triage

JOR Framework v3 is a reference implementation of the James Orion Report (JOR) Bayesian Fusion Framework, a structured probabilistic system for evaluating UAP cases. The framework separates: Solid Object Probability (SOP) — the probability that an observed event involved a persistent, structured, physically real object Non-Human Probability (NHP) — a conditional anomaly-weighted score indicating deviation from conventional human or natural explanations This separation prevents conflating anomalous observations with unsupported origin claims. The framework supports: Government and institutional triage of UAP cases Historical case reevaluation Research into technological surprise detection Internal analytic consistency checks The tool allows analysts to enter rubric-based scoring for witness credibility, environmental conditions, and physical/sensor evidence, producing posterior probabilities for SOP and NHP. Manual overrides are supported for legacy cases or sensitivity testing. Note: This software is decision-support only and is not intended to assert extraterrestrial origin. The software source code is released under the MIT License; documentation and written materials are licensed under CC-BY 4.0. For full guidance, see the included JOR Framework v3 Organizational User Manual.GitHub repository: https://github.com/jamesorion6869/JOR-Framework-v3 Probabilistic Implementation (PyMC Integration) A reproducible Bayesian implementation of the JOR framework using PyMC is available: GitHub repository: https://github.com/jamesorion6869/JOR_Framework_PyMC A reproducible Bayesian implementation of the JOR Framework using PyMC is available to support posterior estimation, uncertainty quantification, and reproducible probabilistic analysis.This implementation enables:• posterior distribution estimation • uncertainty quantification and credible intervals• reproducible Bayesian inference workflows• sensitivity analysis and model transparency The repository demonstrates how structured JOR evidence outputs can be integrated with probabilistic programming to support scientific reproducibility and future hierarchical modeling research. Name: Jake James (James Orion)Affiliation: Independent ResearcherEmail: spaceydayz2@yahoo.com