{"id":"W4415452645","doi":"10.32370/ia_2025_03_3","title":"Extending the Entropic Potential of Events for Uncertainty Quantification and Decision-Making in Artificial Intelligence","year":2025,"lang":"","type":"article","venue":"Intellectual Archive","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Interpretability; Counterfactual thinking; Uncertainty quantification; Entropy (arrow of time); Bayesian probability; Uncertainty analysis; Bridging (networking); Anomaly detection","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001287852,0.0002763424,0.0003740303,0.0005628688,0.000527768,0.0001838421,0.001016549,0.0001036478,0.00003135412],"category_scores_gemma":[0.003205681,0.0002294015,0.0001532757,0.0008916553,0.000319204,0.0001659225,0.0004948442,0.0004541675,0.000008602609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007721895,"about_ca_system_score_gemma":0.0003393454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001222171,"about_ca_topic_score_gemma":0.0001447503,"domain_scores_codex":[0.9970855,0.0003370167,0.001036122,0.0007370578,0.0002994095,0.0005049635],"domain_scores_gemma":[0.9867977,0.0121963,0.0003163762,0.0004821419,0.0001526744,0.00005478774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000660982,0.0001133094,0.0002809512,0.0001120163,0.00005739202,0.000003757813,0.01483363,0.05117154,0.001038047,0.07918134,0.0001418181,0.8524052],"study_design_scores_gemma":[0.0000939052,0.0001777498,0.0007768975,0.001394579,0.00002828071,0.000005289663,0.0007520471,0.6641501,0.0009770093,0.3313855,0.000113861,0.0001448244],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1095451,0.001105539,0.8869286,0.0006077284,0.0009736557,0.0007099608,0.00003280038,0.0000190541,0.00007757979],"genre_scores_gemma":[0.9723609,0.0001999739,0.02719281,0.00009473933,0.00007069463,0.00004005847,0.000007476402,0.00001067695,0.00002268419],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8628158,"threshold_uncertainty_score":0.9354721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02773244162948347,"score_gpt":0.312364683516739,"score_spread":0.2846322418872556,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}