{"id":"W3124221617","doi":"","title":"Viewpoint: Boosting Recessions","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Recession; Boosting (machine learning); Econometrics; Business cycle; Predictive power; Economics; Computer science; Artificial intelligence; Keynesian economics; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005031979,0.0008974714,0.001084861,0.001448603,0.0009135982,0.002464843,0.00110673,0.001727903,0.0076755],"category_scores_gemma":[0.02177476,0.0004066745,0.0008170374,0.0009546668,0.001125872,0.002927874,0.001396505,0.00381243,0.003815527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008993226,"about_ca_system_score_gemma":0.001058651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002048919,"about_ca_topic_score_gemma":0.002239394,"domain_scores_codex":[0.9987128,0.0005332126,0.00003811075,0.0003262141,0.0002785503,0.0001110889],"domain_scores_gemma":[0.9947338,0.002657308,0.000559981,0.0006640034,0.001028449,0.0003564358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009838365,0.0002241191,0.02893122,0.0005213772,0.0002918164,0.0001931123,0.0007554771,0.06135182,0.003733617,0.09301884,0.09318706,0.7168077],"study_design_scores_gemma":[0.0003740536,0.0008906546,0.02567412,0.0007579944,0.0004126123,0.0007706948,0.0007014523,0.558318,0.00814255,0.2454446,0.1583144,0.000198884],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"commentary","genre_scores_codex":[0.1423234,0.01628704,0.7080861,0.04592725,0.006033313,0.0003158156,0.001789695,0.005912594,0.07332479],"genre_scores_gemma":[0.8092442,0.003631588,0.1494214,0.009011401,0.004369682,0.0002157011,0.001121592,0.0007434001,0.0222411],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.0076755,"threshold_uncertainty_score":0.02661192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03673086833785477,"score_gpt":0.2234411849077032,"score_spread":0.1867103165698484,"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."}}