{"id":"W4394290402","doi":"10.6084/m9.figshare.21420412.v1","title":"Bayes factors and posterior estimation: Two sides of the very same coin","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Statistical Methods and Inference","field":"Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007486816,0.0001914322,0.0003466274,0.0000375373,0.00009495443,0.00004327654,0.0003300197,0.00008707357,0.552896],"category_scores_gemma":[0.01546102,0.0001244758,0.00006982023,0.00009322744,0.00002343093,0.00003501231,0.0004829982,0.0002802155,0.0000279227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002311036,"about_ca_system_score_gemma":0.00007715314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005770874,"about_ca_topic_score_gemma":0.00003513149,"domain_scores_codex":[0.9989762,0.0001611128,0.0002797606,0.0001930248,0.0002651674,0.0001247847],"domain_scores_gemma":[0.9955347,0.003640597,0.0002889029,0.000435434,0.00005644809,0.00004390661],"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":[0.000002994904,0.00001555435,0.000007716282,0.0009725557,0.00001698193,0.000002933499,0.00003398136,4.198635e-7,0.000001530167,0.00009952003,0.9980512,0.0007946512],"study_design_scores_gemma":[0.0002375092,0.0001376439,0.002114889,0.00332068,0.0001313488,0.0000217467,0.00009273083,0.0001309609,0.0001947118,0.0262796,0.9669198,0.0004184119],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001193468,0.00008280933,0.000006170857,0.00002267957,0.00009522956,0.0002298224,0.9993813,0.00001328339,0.00004937463],"genre_scores_gemma":[0.00005133263,0.000002894855,0.003912678,0.00007594871,0.00002766855,0.00006852829,0.9958122,0.00001410297,0.00003465831],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5528681,"threshold_uncertainty_score":0.9928322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1358870573780561,"score_gpt":0.3839441119088302,"score_spread":0.2480570545307741,"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."}}