{"id":"W4233480015","doi":"10.1515/iupac.81.0092","title":"Bayesian Probability","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Ecotoxicology; Bayesian probability; Relation (database); Computer science; Ecology; Biology; Data mining; Artificial intelligence; Linguistics; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004404192,0.003484188,0.002278009,0.005523282,0.001293921,0.004928732,0.006097721,0.004278176,0.06584335],"category_scores_gemma":[0.02765137,0.001112847,0.003211608,0.007685444,0.0008738301,0.003683943,0.002646151,0.004943314,0.09465573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003715342,"about_ca_system_score_gemma":0.003331746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0277419,"about_ca_topic_score_gemma":0.05048768,"domain_scores_codex":[0.9960995,0.001227882,0.0003668658,0.001236192,0.0007704614,0.000299147],"domain_scores_gemma":[0.9913579,0.00431623,0.0004156897,0.002239476,0.001359557,0.0003112532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001434989,0.00007635695,0.002645374,0.0008081269,0.0001162256,0.00005117141,0.00002810561,0.005542666,0.00006680301,0.003083974,0.9613454,0.02609234],"study_design_scores_gemma":[0.0005040193,0.0000671535,0.00504603,0.0008921189,0.0001232839,0.0004700101,0.0001214388,0.03127429,0.0007627609,0.04515842,0.9154704,0.0001100477],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009835567,0.001282386,0.007050298,0.0009054933,0.0002503765,0.0001365987,0.9818602,0.003191635,0.00433953],"genre_scores_gemma":[0.00336174,0.0005469084,0.009085454,0.0003021971,0.00005848584,0.0004141303,0.9835292,0.0002699498,0.002431877],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06584335,"threshold_uncertainty_score":0.220268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01867095943699669,"score_gpt":0.3883203934184031,"score_spread":0.3696494339814064,"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."}}