{"id":"W3090116485","doi":"10.3386/w27881","title":"The Distribution of COVID-19 Related Risks","year":2020,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Bank of Canada; Université du Québec à Montréal; University of British Columbia","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Distribution (mathematics); Demographic economics; Population; Business; Counterfactual thinking; Matching (statistics); Economics; Environmental health; Medicine; Psychology; Social psychology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002151189,0.0002897628,0.0003165652,0.003224432,0.0007071334,0.002131104,0.0008399544,0.0004873159,0.005408285],"category_scores_gemma":[0.01509554,0.0002064038,0.0005037121,0.004309502,0.0009426892,0.0006179028,0.001322942,0.001186039,0.0004792305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007288242,"about_ca_system_score_gemma":0.005781927,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.6215383,"about_ca_topic_score_gemma":0.6095612,"domain_scores_codex":[0.9962836,0.0004098579,0.0001733264,0.0004090318,0.001975959,0.0007483528],"domain_scores_gemma":[0.993149,0.001516658,0.001752808,0.0004905411,0.00253266,0.0005584031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001461028,0.00005072657,0.9357911,0.00007326358,0.0001352235,0.000153157,0.001147026,0.005873587,0.0005354374,0.01600544,0.006626104,0.03346282],"study_design_scores_gemma":[0.000007141216,0.00004216395,0.9721142,0.0000656793,0.00003362107,0.0002559546,0.001884243,0.008019296,0.0003506691,0.003948086,0.01323016,0.00004874926],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9094588,0.003127081,0.007999055,0.002956753,0.00007213034,0.0002856826,0.03521679,0.0001309398,0.04075275],"genre_scores_gemma":[0.9819881,0.001443737,0.001267367,0.0001072676,0.00002616941,0.00004814677,0.009649699,0.00001965327,0.00544973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6215383,"threshold_uncertainty_score":0.761381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5561006275870051,"score_gpt":0.5288432158955723,"score_spread":0.02725741169143281,"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."}}