{"id":"W2089163081","doi":"10.1002/jae.1018","title":"Learning, forecasting and structural breaks","year":2008,"lang":"en","type":"article","venue":"Journal of Applied Econometrics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Prior probability; Bayesian probability; Econometrics; Computer science; Series (stratigraphy); Bayesian inference; Structural break; Economics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007474381,0.0001831741,0.0006474608,0.001111936,0.0002136738,0.00006400833,0.0002135592,0.0001158054,0.0003928265],"category_scores_gemma":[0.0001879502,0.0001995533,0.0001463218,0.0003555561,0.00009680701,0.0004167832,0.00005910277,0.0004086409,0.0001058345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001123267,"about_ca_system_score_gemma":0.00001956499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003848131,"about_ca_topic_score_gemma":0.000001285333,"domain_scores_codex":[0.9982334,0.000006110324,0.001136628,0.0002449563,0.00003404917,0.0003447942],"domain_scores_gemma":[0.9981316,0.000140825,0.001341386,0.0001441741,0.00001895822,0.0002230834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003264241,0.0001217741,0.8506693,0.0001241503,0.000705372,0.0001028988,0.006757967,0.04929649,0.00002578795,0.05608421,0.004595104,0.03119048],"study_design_scores_gemma":[0.008010179,0.001841498,0.5736473,0.00003691226,0.00007674985,0.005834509,0.001111343,0.1368587,0.0003071546,0.1214697,0.1485969,0.002209018],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.964394,0.001304217,0.000492879,0.00010518,0.000309473,0.00007528259,0.00001965397,0.00001180416,0.03328755],"genre_scores_gemma":[0.9962056,0.0007449158,0.002144885,0.0001856613,0.000382135,0.00000141266,0.000003114817,0.00002592132,0.0003063829],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.277022,"threshold_uncertainty_score":0.8137548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1414220987614072,"score_gpt":0.210449013542516,"score_spread":0.06902691478110887,"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."}}