{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0054067,0.0004542468,0.001360952,0.00006715325,0.0005513345,0.00009722061,0.0008725542,0.0002749632,0.001518446],"category_scores_gemma":[0.04105009,0.0003026544,0.0005032231,0.0001285855,0.0002295774,0.0001003308,0.001644881,0.001447488,0.000133304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002513569,"about_ca_system_score_gemma":0.002433107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001452348,"about_ca_topic_score_gemma":0.002654803,"domain_scores_codex":[0.9963425,0.0007922911,0.001366382,0.0008680154,0.0003409399,0.0002899165],"domain_scores_gemma":[0.993474,0.004394385,0.001126765,0.0006024673,0.0002148164,0.0001875422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001789008,0.0001940576,0.006440392,0.03255618,0.001282815,0.00004842709,0.0005004254,0.02843658,0.000001725073,0.3751732,0.5405595,0.01478877],"study_design_scores_gemma":[0.0001416255,0.00000711376,0.0001995854,0.001769847,0.0001419616,0.00001790671,0.00001219818,0.0008650124,6.649141e-7,0.1136029,0.8829044,0.0003367949],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.00250061,0.4064678,0.002576937,0.5630727,0.003507388,0.003560859,0.0005161766,0.0004135096,0.01738396],"genre_scores_gemma":[0.02203543,0.8692071,0.006078449,0.09494495,0.0013788,0.001580639,0.001470198,0.00007649698,0.003227873],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.4681278,"threshold_uncertainty_score":0.9999425,"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."}}