{"id":"W3096869795","doi":"10.5334/aogh.3104","title":"COVID-19: How to Reduce Some Environmental and Social Impacts?","year":2020,"lang":"en","type":"article","venue":"Annals of Global Health","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Personal protective equipment; Perspective (graphical); Pandemic; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Public health; Health care; Reflection (computer programming); Business; Population; Environmental health; Public relations; Medical emergency; Medicine; Nursing; Political science; Computer science; Economic growth; Economics; Virology","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":[0.01246241,0.001382426,0.001324358,0.002293009,0.003748539,0.009332196,0.003731068,0.005881303,0.0764264],"category_scores_gemma":[0.03529945,0.0003634115,0.001740774,0.001830562,0.003594148,0.008882448,0.007705343,0.008788949,0.01632676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003168481,"about_ca_system_score_gemma":0.01955597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01122855,"about_ca_topic_score_gemma":0.02533644,"domain_scores_codex":[0.991459,0.00388776,0.0005216036,0.0005508669,0.002506488,0.001074267],"domain_scores_gemma":[0.9844609,0.003717171,0.0008848928,0.001006031,0.004379501,0.005551464],"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":[0.0001526404,0.000243125,0.001778521,0.001973469,0.0001175885,0.0002311127,0.0006743781,0.0002165395,0.0002855062,0.0265739,0.7379089,0.2298443],"study_design_scores_gemma":[0.00005188538,0.0001412282,0.001849559,0.002513234,0.00006375751,0.0001849839,0.001606414,0.0001337475,0.000291719,0.02013446,0.972981,0.00004804034],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.002558716,0.04972554,0.004479604,0.8321565,0.03276575,0.0002340308,0.001591004,0.0008316708,0.07565708],"genre_scores_gemma":[0.08610959,0.1410007,0.04027662,0.5665638,0.02450974,0.001312059,0.004181084,0.001575815,0.1344705],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.0764264,"threshold_uncertainty_score":0.2556718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1400231306443504,"score_gpt":0.420821965772762,"score_spread":0.2807988351284116,"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."}}