James Orion Report (JOR) Bayesian Fusion: Evidence-Driven SOP and NHP Analysis of UAP Cases

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: Organizational User Manual — Field Guidance for Data-Driven UAP Case Triage
https://doi.org/10.5281/zenodo.18203566

Contact / Email:
Jake James (James Orion)
spaceydayz2@yahoo.com

Programming Language:
Python