Title:James Orion Report (JOR) Bayesian Fusion: Evidence-Driven SOP and NHP Analysis of UAP Cases Authors:Jake James (James Orion) Executive Summary: The JOR Bayesian Fusion Framework is a civilian-led operational UAP research framework designed for evidence-based triage and multi-source data fusion. The James Orion Report (JOR v3) defines a Structured Probabilistic Triage Framework (SPTF) using Bayesian fusion, providing a reproducible and transparent method for evidence-driven UAP case analysis. It establishes a standardized, probabilistic methodology to prioritize Unidentified Anomalous Phenomena (UAP) reports for scientific and governmental evaluation. Description / Abstract: Purpose: A practical evidentiary triage framework for separating credible solid-object observations from speculative non-human interpretations in safety-critical observational and reporting contexts. Design: Modular and system-agnostic, intended for integration with existing sensor fusion pipelines, analytic workflows, and decision-support systems. Scope: Applicable to aviation and aerospace safety, defense and intelligence reporting, scientific anomaly review, and other environments where evidentiary discipline is required prior to higher-order interpretation. This preprint presents JOR Framework v3, a rigorous methodology for evaluating Unidentified Aerial Phenomena (UAP) using Bayesian posterior analysis. This Bayesian evidence fusion framework combines the James Orion Report (JOR) system with probabilistic reasoning to provide structured, evidence-driven analysis. The methodology quantifies: Solid Object Probability (SOP): the likelihood that a physical event occurred Non-Human Probability (NHP): an anomaly-weighted score indicating deviation from conventional human or natural explanations By integrating witness credibility, environmental conditions, and physical/sensor evidence, the framework produces weighted SOP and NHP scores. Bayesian updating then calculates posterior probabilities, reflecting both prior knowledge and observed evidence. This ensures that non-human hypotheses are evaluated only on a solid evidentiary foundation. v3 updates: formatting corrections, added Limitations and Future Work section, and corrected human-likelihood formula. Two illustrative cases demonstrate practical application: Tier 1 UAP — Aguadilla, Puerto Rico (2013) Tier 2 UAP — Socorro, New Mexico (1964) JOR Framework v3 supports reproducible, transparent, and systematic analysis, enabling automated evaluation, rapid assessment of new cases, and integration with probabilistic programming tools for future research. Clarification: The JOR framework is a triage-oriented probabilistic system, designed to work under conditions of incomplete data and information asymmetry. Several design choices—like using bounded heuristic fusion operators, agency-specific parameterization, and qualitative scoring rubrics—are intentional constraints, not unresolved limitations. Sensitivity analyses show robustness to changes in priors and parameters, and the framework supports transparent calibration when institutional data is available. These features make the framework both auditable and adaptable across different operational contexts, while keeping triage decisions interpretable and defensible. This isn’t meant to be a final attribution system—rather, it’s a tool to help prioritize cases for deeper analysis. Keywords:UAP, Bayesian Fusion, SOP, NHP, probabilistic framework, decision support, evidence-driven analysis Version:v3 Related Works: JOR Framework v3.1: Organizational User Manual — Bayesian Fusion Engine with Stochastic Flight Modeling for UAP Case Triage https://doi.org/10.5281/zenodo.19688346 JOR Framework v3: Organizational User Manual — Field Guidance for Data-Driven UAP Case Triage https://doi.org/10.5281/zenodo.18203566 Probabilistic Implementation (PyMC Integration – v3.1 Engine) A reproducible Bayesian implementation of the JOR framework (v3.1) is available: JOR_PYMC_V3_1 GitHub Repository https://github.com/jamesorion6869/JOR_PYMC_V3_1 This implementation introduces the JOR v3.1 probabilistic engine, featuring: • stochastic flight modeling using truncated normal distributions • posterior distribution estimation (MCMC / variational inference) • uncertainty quantification with credible intervals • sensitivity analysis and model transparency • improved evidentiary calibration of anomaly weighting The repository demonstrates how structured JOR evidence outputs can be integrated with modern probabilistic programming to support reproducible, uncertainty-aware analysis and future hierarchical modeling research. Version Note This Zenodo record documents JOR Framework v3, with methodological extensions implemented in v3.1. Users are encouraged to reference the v3.1 implementation for the most current probabilistic modeling capabilities. Contact / Email:Jake James (James Orion)spaceydayz2@yahoo.com Repository URL: https://github.com/jamesorion6869/JOR_PYMC_V3_1⁠ Programming Language:Python

Status: Project Complete — v3.1 Final Release This update reflects the implementation of the JOR Bayesian Fusion framework using the PyMC probabilistic programming library. The prior probabilities are based on contemporary UAP reporting and are deliberately conservative. Alternative priors or weighting schemes can be substituted without altering the underlying structure, allowing for sensitivity testing and comparative analysis across assumptions. Following the initial conceptual formulation, the core Bayesian updating loop has been implemented and automated using PyMC in Python. This transitions the framework from manual illustrative calculations to continuous random variables and MCMC-based sampling. While case ingestion remains manually scored to preserve its role as a structured decision-support layer, the Python implementation establishes a scalable probabilistic architecture capable of handling larger pre-scored UAP datasets, enabling rapid sensitivity analysis and reducing calculation error. The repository further extends outputs into operational aviation safety metrics via a risk-binning architecture, including flight hazard and collision risk components. The open-source implementation and supporting modules are available in the project repository.