{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.111883,0.002304854,0.004674617,0.008312617,0.002374427,0.01764847,0.006168766,0.006658011,0.01601322],"category_scores_gemma":[0.403925,0.001774594,0.002299604,0.01675249,0.01496094,0.0221172,0.007040696,0.01500768,0.006597446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004367142,"about_ca_system_score_gemma":0.00554958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005657384,"about_ca_topic_score_gemma":0.004901688,"domain_scores_codex":[0.869333,0.1045606,0.005860559,0.008115199,0.01105303,0.001077686],"domain_scores_gemma":[0.5056323,0.4361277,0.009455272,0.03391109,0.01312063,0.001753042],"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":[0.0005155473,0.00005823863,0.005192914,0.0015303,0.0005466499,0.0001795893,0.001168167,0.003762985,0.0001029583,0.7043295,0.1598402,0.1227729],"study_design_scores_gemma":[0.000147389,0.00001680442,0.0009396831,0.001126364,0.00009621789,0.0001840791,0.0001622755,0.003511861,0.0001681402,0.9071946,0.08636224,0.00009047027],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.005076796,0.05536868,0.7110874,0.1603918,0.00708854,0.0003505218,0.02115555,0.001698849,0.03778185],"genre_scores_gemma":[0.2120605,0.05287284,0.6180409,0.05249602,0.0171257,0.002966842,0.02816577,0.006350236,0.009921172],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.111883,"threshold_uncertainty_score":0.5917008,"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."}}