{"id":"W2066073817","doi":"10.1017/s1365100508080024","title":"A BAYESIAN CLASSIFICATION APPROACH TO MONETARY AGGREGATION","year":2009,"lang":"en","type":"article","venue":"Macroeconomic Dynamics","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Divisia monetary aggregates index; Bayesian probability; Construct (python library); Economics; Divisia index; Econometrics; Set (abstract data type); Monetary policy; Central bank; Mathematical economics; Computer science; Artificial intelligence; Mathematics; Macroeconomics; Statistics; Quantitative easing","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":[0.006064965,0.0008152945,0.001733471,0.005263812,0.001530817,0.003197032,0.001824084,0.001676851,0.002504556],"category_scores_gemma":[0.01938334,0.0007677993,0.001682793,0.003844707,0.001373433,0.00397857,0.00192368,0.00249008,0.0007839701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002144298,"about_ca_system_score_gemma":0.001396557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007720765,"about_ca_topic_score_gemma":0.00759168,"domain_scores_codex":[0.9957621,0.001900286,0.0003182526,0.0007857299,0.00101212,0.0002216247],"domain_scores_gemma":[0.9936463,0.003691255,0.0006978407,0.0007236713,0.001073687,0.0001673769],"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":[0.0001310334,0.0001791615,0.007577435,0.0001665655,0.0002894543,0.0001346839,0.0006261401,0.1800748,0.001017362,0.494525,0.005906345,0.3093721],"study_design_scores_gemma":[0.00001977837,0.00002060695,0.001820108,0.00005152508,0.00004880651,0.00006510678,0.00004834059,0.6526077,0.0003760181,0.3403421,0.004562507,0.00003740918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008541753,0.0005014532,0.9872987,0.0005377702,0.00007050274,0.00005274689,0.0001836045,0.0001676131,0.002645927],"genre_scores_gemma":[0.3205914,0.001073012,0.6710871,0.0003973607,0.0006597802,0.0003855912,0.00121327,0.0000982457,0.004494226],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007720765,"threshold_uncertainty_score":0.03207499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01961341277444528,"score_gpt":0.2063977569272538,"score_spread":0.1867843441528085,"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."}}