{"id":"W4394288321","doi":"10.6084/m9.figshare.21420412","title":"Bayes Factors and Posterior Estimation: Two Sides of the Very Same Coin","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bayes' theorem; Estimation; Statistics; Mathematics; Econometrics; Computer science; Bayesian probability; Artificial intelligence; Economics","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":[0.1240387,0.002234559,0.004492902,0.009738665,0.002312443,0.0175457,0.004990538,0.006884293,0.00915872],"category_scores_gemma":[0.372291,0.001567875,0.00224493,0.01618635,0.01731313,0.02428588,0.00657144,0.01563673,0.003743423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004426846,"about_ca_system_score_gemma":0.005345773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005683756,"about_ca_topic_score_gemma":0.004219956,"domain_scores_codex":[0.8686675,0.1017639,0.006755534,0.007916448,0.01365554,0.001241101],"domain_scores_gemma":[0.5426686,0.4016086,0.01052712,0.02660948,0.01664593,0.001940134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004283939,0.00006122397,0.005737553,0.001173721,0.0004960066,0.0001675621,0.001255272,0.004147895,0.00010671,0.7691668,0.07345416,0.1438047],"study_design_scores_gemma":[0.00009359531,0.000021418,0.001062074,0.00110212,0.00008726979,0.0001394569,0.0002113338,0.004444547,0.0001728637,0.9451869,0.04738968,0.00008878699],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.006822659,0.0627125,0.7111415,0.1668366,0.007564378,0.0003318151,0.007900124,0.0009569978,0.03573325],"genre_scores_gemma":[0.2990301,0.06432766,0.5480157,0.04042494,0.02340398,0.002255453,0.01108612,0.003065854,0.008390289],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.1240387,"threshold_uncertainty_score":0.6559871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.163806655146096,"score_gpt":0.3929336541119689,"score_spread":0.2291269989658729,"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."}}