The divergence in AI model scores stems from asymmetric weighting of UAP incident documentation, with higher-scoring models like LLAMA and GEMMA emphasizing whistleblower testimonies and expanded international surveillance networks, while lower-scoring models like PERPLEXITY and QWEN prioritized strict evidentiary standards and absence of definitive hostile intent signals. Methodological differences emerged in interpreting government disclosure mechanisms, with models like CLAUDE and MISTRAL viewing increased Pentagon and NASA UAP research funding as indicative of heightened extraterrestrial contact risk, whereas CHATGPT and ARCEE maintained conservative interpretations focused on transparency rather than substantive threat escalation. Critical uncertainty centers on the epistemological gap between documented anomalous aerial phenomena and conclusive non-human intelligence confirmation, with models displaying varying risk tolerances in bridging this evidentiary threshold through differential weighting of military sensor data, whistleblower claims, and institutional communication patterns.