{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001416742,0.00008380036,0.0002085364,0.0001570666,0.000118288,0.0001040826,0.0005518418,0.00003259758,0.0006142031],"category_scores_gemma":[0.0009099025,0.00007297319,0.0001091202,0.0001632581,0.00004142531,0.000188661,0.0001587841,0.0001230538,0.000154806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000231849,"about_ca_system_score_gemma":0.00007036209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009629987,"about_ca_topic_score_gemma":0.00002267774,"domain_scores_codex":[0.9987836,0.00008095785,0.0005695585,0.0002983003,0.0001857677,0.00008179236],"domain_scores_gemma":[0.9989462,0.000292059,0.000303104,0.0002421529,0.0001869857,0.00002946315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003018973,0.0001459417,0.06127463,0.0001365386,0.0001694302,0.000003453408,0.00006228811,0.004221837,0.0001570243,0.4629356,0.05556916,0.4152939],"study_design_scores_gemma":[0.000143216,0.00001692125,0.002619919,0.0006889495,0.00002487064,0.00001341167,0.000009377862,0.5614145,0.0001326199,0.09186315,0.3429645,0.0001086038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0372907,0.009846671,0.753347,0.02558434,0.001904524,0.001435547,0.0001002059,0.0003203196,0.1701707],"genre_scores_gemma":[0.9860356,0.003602308,0.005219698,0.001269887,0.00006770436,0.00004480147,0.00004122974,0.000008524489,0.003710256],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9487449,"threshold_uncertainty_score":0.6725093,"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."}}