{"id":"W4417167884","doi":"10.48550/arxiv.2504.20216","title":"Flexible extreme thresholds through generalised Bayesian model averaging","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Sensitivity (control systems); Threshold model; Model selection; Cover (algebra); Term (time); Bayesian inference; Selection (genetic algorithm); Mixture model; Posterior probability","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009059471,0.001413235,0.002571141,0.002650921,0.0007322905,0.00248309,0.003199512,0.001748481,0.00178544],"category_scores_gemma":[0.0221536,0.001031476,0.002456491,0.001968614,0.001498855,0.002469518,0.002978891,0.002697083,0.0006051078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001340763,"about_ca_system_score_gemma":0.001452565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007122106,"about_ca_topic_score_gemma":0.006204031,"domain_scores_codex":[0.9954348,0.002503537,0.0002134013,0.0008311665,0.000774277,0.0002428004],"domain_scores_gemma":[0.9915063,0.006414277,0.0006679439,0.0006755941,0.0005515229,0.0001844547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006773503,0.00003334263,0.00145944,0.00006601367,0.0001630585,0.000101705,0.0001374589,0.9158401,0.0007316238,0.03202811,0.001095912,0.04827552],"study_design_scores_gemma":[0.000005669473,0.00001143209,0.0001740648,0.000008823216,0.00001424315,0.0000169271,0.000007189222,0.9655434,0.0001565833,0.03373352,0.0003175731,0.00001067039],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007747936,0.00017207,0.9910651,0.0001035644,0.00001119467,0.00002936029,0.0000557758,0.0003466901,0.0004682542],"genre_scores_gemma":[0.5296425,0.0005311408,0.4658284,0.000260284,0.0001295277,0.0003923065,0.0008271291,0.0003888354,0.001999979],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009059471,"threshold_uncertainty_score":0.04791164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.425775411241397,"score_gpt":0.4191508606797159,"score_spread":0.006624550561681097,"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."}}