{"id":"W3123105361","doi":"10.1007/s10272-021-0948-y","title":"COVID-19: Lockdowns, Fatality Rates and GDP Growth","year":2021,"lang":"en","type":"article","venue":"Intereconomics","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Coronavirus disease 2019 (COVID-19); Social distance; Case fatality rate; Economics; Pandemic; Demographic economics; Panel data; 2019-20 coronavirus outbreak; Endogeneity; Sample (material); Development economics; Econometrics; Geography; Medicine; Population; Environmental health; Virology","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":[0.0008249599,0.000364012,0.000274557,0.0009885456,0.000255922,0.00130758,0.000239173,0.0004669625,0.002470315],"category_scores_gemma":[0.003995814,0.0001104537,0.0004560309,0.001273975,0.0006471126,0.0008502156,0.001384993,0.001377664,0.0004147638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000648943,"about_ca_system_score_gemma":0.0005949521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01601734,"about_ca_topic_score_gemma":0.009793865,"domain_scores_codex":[0.999631,0.00006517694,0.00002826036,0.00005024073,0.00006941636,0.0001557947],"domain_scores_gemma":[0.9965482,0.0006577525,0.002000954,0.0001122019,0.0002636328,0.0004172588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000162791,0.00004999834,0.9712956,0.0001117054,0.0001719376,0.000311355,0.0002239781,0.008838437,0.0003405263,0.002612621,0.005180349,0.01070073],"study_design_scores_gemma":[0.0000141053,0.0001793257,0.9779077,0.0000955483,0.00006088265,0.0002192715,0.001540671,0.008103776,0.0008974866,0.001716012,0.009230232,0.00003492348],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842545,0.001724447,0.0008427324,0.001423847,0.00008830711,0.00001569885,0.006954277,0.00005293919,0.004643342],"genre_scores_gemma":[0.9936674,0.0005116275,0.0001199487,0.00008086133,0.00003776092,0.000008219015,0.004808549,0.000006748397,0.0007588093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01601734,"threshold_uncertainty_score":0.03184825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07761069914107353,"score_gpt":0.296266604690912,"score_spread":0.2186559055498384,"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."}}