{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001845976,0.0006024175,0.0008257492,0.0002183476,0.0001571254,0.0002261185,0.002464257,0.0005581802,0.0005808325],"category_scores_gemma":[0.0005207652,0.0004221897,0.000295973,0.0003689885,0.0001649387,0.0003362113,0.0007690676,0.0006528673,0.000002891097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004048605,"about_ca_system_score_gemma":0.001354264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000516833,"about_ca_topic_score_gemma":0.0002123961,"domain_scores_codex":[0.9957305,0.000402602,0.0006367798,0.001260559,0.001267375,0.0007022339],"domain_scores_gemma":[0.9955872,0.0001702474,0.0003272063,0.003037422,0.0005246655,0.0003533121],"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":[0.00001727495,0.0001490011,9.405096e-7,0.0001064254,0.00003864785,0.00005005761,0.00001216646,2.275545e-7,0.000003002823,0.001966706,0.9149188,0.08273669],"study_design_scores_gemma":[0.0004127974,0.0001451102,0.000005029154,0.0002104022,0.00003203092,0.00002690195,4.500749e-7,0.0001416412,0.00002140537,0.07631458,0.9221637,0.0005259088],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[3.332588e-7,0.0002185847,0.493407,0.001054077,0.0006622028,0.0002397673,0.5042266,0.0001096639,0.00008181548],"genre_scores_gemma":[0.000002139466,0.0002445084,0.1357026,0.000649554,0.000771778,0.00002813177,0.8622398,0.00002865033,0.0003328253],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3580132,"threshold_uncertainty_score":0.999823,"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."}}