{"id":"W1662363606","doi":"10.48550/arxiv.1104.3258","title":"Inferences from prior-based loss functions","year":2011,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Bayes' theorem; Limiting; Bayesian probability; Bayes factor; Inference; Prior probability; Econometrics; Bayesian inference; Statistics; Computer science; Mathematics; Artificial intelligence; Engineering","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":[0.01948513,0.001737284,0.001824678,0.003817115,0.001387893,0.007669797,0.002738499,0.002784038,0.006782058],"category_scores_gemma":[0.1373127,0.001032217,0.00174573,0.002325919,0.00348877,0.01011428,0.00364451,0.007045308,0.001394716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002808697,"about_ca_system_score_gemma":0.00185763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001524114,"about_ca_topic_score_gemma":0.001319427,"domain_scores_codex":[0.99072,0.005117326,0.0003434155,0.001153959,0.002257482,0.0004077131],"domain_scores_gemma":[0.9284186,0.062647,0.002744991,0.003469938,0.00211807,0.0006012985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000146011,0.00009239111,0.002044807,0.0001954279,0.0001164745,0.0001862316,0.0002029886,0.1192787,0.0008384174,0.8288794,0.003209033,0.04481013],"study_design_scores_gemma":[0.00001620908,0.00001845565,0.0003785403,0.00007496161,0.00002623772,0.00006839615,0.00004280178,0.2378103,0.0007620067,0.7594962,0.001279902,0.00002605216],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01491159,0.0005344102,0.9753185,0.001140488,0.00008013665,0.00005252268,0.0001876226,0.0002207217,0.00755391],"genre_scores_gemma":[0.6435959,0.002860008,0.3408849,0.001291835,0.0007271638,0.0005064026,0.0009520805,0.0005865595,0.008595107],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01948513,"threshold_uncertainty_score":0.1030484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1330529445005447,"score_gpt":0.1925080916999274,"score_spread":0.05945514719938269,"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."}}