{"id":"W3150044319","doi":"10.2139/ssrn.3809864","title":"The Impact of Early or Late Lockdowns on the Spread of COVID-19 in US Counties","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; National Research Council Canada; University of Saskatchewan; University of Victoria","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Geography; Demographic economics; Economics; Virology; Medicine; Outbreak; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.003399929,0.0001773583,0.0003866258,0.0009761063,0.000916508,0.001794514,0.0008999857,0.001157455,0.007358243],"category_scores_gemma":[0.02272519,0.0003052281,0.0006030736,0.001343372,0.0009019421,0.001314328,0.002551499,0.002297512,0.0003827313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001421544,"about_ca_system_score_gemma":0.002023784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1080943,"about_ca_topic_score_gemma":0.1255693,"domain_scores_codex":[0.9963524,0.001497845,0.0001798327,0.0003577707,0.0002266497,0.001385551],"domain_scores_gemma":[0.9810401,0.006122551,0.006709299,0.0008919008,0.001500968,0.003735252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008910461,0.0002486231,0.989395,0.00004408857,0.0001696385,0.00009728246,0.0008645957,0.0006522823,0.0001479267,0.001149855,0.001224355,0.005115343],"study_design_scores_gemma":[0.00001836114,0.0002662014,0.99506,0.0000433939,0.00007450678,0.00003144225,0.003012453,0.0005515188,0.00007545366,0.0002545774,0.0006018783,0.00001014307],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957263,0.0005644417,0.00009842401,0.001153123,0.00002863581,0.00001250693,0.0008197532,0.000004323307,0.001592465],"genre_scores_gemma":[0.9988488,0.0001644271,0.00002966129,0.00009327311,0.00001878919,0.00000675826,0.0003369039,0.000001982411,0.0004992548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1080943,"threshold_uncertainty_score":0.2149301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1299999300258854,"score_gpt":0.4179357717999561,"score_spread":0.2879358417740707,"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."}}