{"id":"W3140816121","doi":"10.1017/nie.2021.10","title":"CAN MACHINE LEARNING CATCH THE COVID-19 RECESSION?","year":2021,"lang":"en","type":"preprint","venue":"National Institute Economic Review","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations; Université du Québec à Montréal","funders":"Université du Québec à Montréal","keywords":"Coronavirus disease 2019 (COVID-19); Recession; Sample (material); Computer science; Set (abstract data type); Econometrics; Artificial intelligence; Machine learning; Nonlinear system; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Pandemic; 2019-20 coronavirus outbreak; Great recession; Data set; Economics; Macroeconomics; Keynesian economics; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0130649,0.0008040128,0.001390943,0.001860303,0.0007104466,0.002642402,0.001157662,0.002188222,0.007023545],"category_scores_gemma":[0.07419767,0.0003161854,0.0006638273,0.002037386,0.002295107,0.01106488,0.002035707,0.005739897,0.001707068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001969214,"about_ca_system_score_gemma":0.002031991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009070355,"about_ca_topic_score_gemma":0.007345559,"domain_scores_codex":[0.9979183,0.001250228,0.00009973568,0.0002611816,0.0002992848,0.0001712978],"domain_scores_gemma":[0.9779422,0.01496669,0.002386576,0.001888958,0.002392378,0.0004232559],"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":[0.0004252016,0.0001059056,0.01457996,0.001213518,0.0005300303,0.0002042352,0.0006110003,0.01593841,0.0002264785,0.5436234,0.1299852,0.2925566],"study_design_scores_gemma":[0.00009061457,0.0001306381,0.01502579,0.001290315,0.000116418,0.00008271965,0.0005946314,0.02367797,0.0002983477,0.8533815,0.1052639,0.00004724717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.04721655,0.09546799,0.0844755,0.7120953,0.008708912,0.00009790683,0.002338026,0.0005681592,0.04903173],"genre_scores_gemma":[0.7964121,0.07565356,0.02718843,0.06392204,0.01657571,0.0002743582,0.001651458,0.0004715586,0.01785077],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0130649,"threshold_uncertainty_score":0.06909466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4176817409831661,"score_gpt":0.4879320510373772,"score_spread":0.07025031005421106,"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."}}