{"id":"W4408386334","doi":"10.1111/iere.12759","title":"PREDICTIVE DENSITY COMBINATION USING BAYESIAN MACHINE LEARNING","year":2025,"lang":"en","type":"article","venue":"International Economic Review","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"Austrian Science Fund","keywords":"Bayesian probability; Machine learning; Artificial intelligence; Computer science; Bayesian inference","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":[0.006253939,0.00109833,0.001706977,0.002760828,0.00051984,0.002075928,0.001676898,0.001258008,0.00384931],"category_scores_gemma":[0.01805872,0.0007765346,0.00137385,0.002041069,0.001211594,0.003309861,0.001708375,0.001902594,0.0005785449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001528551,"about_ca_system_score_gemma":0.0008492331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003795018,"about_ca_topic_score_gemma":0.002374389,"domain_scores_codex":[0.9966435,0.001898583,0.0001451754,0.0003612694,0.0008378667,0.0001136479],"domain_scores_gemma":[0.9927822,0.005374432,0.0005204555,0.0004309211,0.000782393,0.0001094905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001141789,0.00005690342,0.001190739,0.0001492082,0.0002300699,0.0001142344,0.0001563643,0.7385654,0.0009503487,0.1240116,0.002148299,0.1323127],"study_design_scores_gemma":[0.00000739734,0.00001025806,0.0001218499,0.00001822007,0.00001581762,0.000009375125,0.000008398808,0.9402056,0.0002154583,0.0588402,0.0005366934,0.00001081938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008772107,0.0004347499,0.9864553,0.0003859061,0.00005569035,0.00004361351,0.00008062215,0.0003126522,0.003459337],"genre_scores_gemma":[0.6839297,0.0006494669,0.3122489,0.0002626041,0.0002593596,0.0002009144,0.0003697888,0.0001045459,0.001974856],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006253939,"threshold_uncertainty_score":0.03307438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08578703631450098,"score_gpt":0.4108994932946922,"score_spread":0.3251124569801912,"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."}}