{"id":"W3173598369","doi":"10.1101/2021.06.14.21258894","title":"Widespread Contamination of SARS-CoV-2 on Highly Touched Surfaces in Brazil During the Second Wave of the COVID-19 Pandemic","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Infection Control and Ventilation","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco; International Development Research Centre","keywords":"Contamination; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus disease 2019 (COVID-19); Pandemic; Transmission (telecommunications); Geography; 2019-20 coronavirus outbreak; Public health; Temperate climate; Environmental health; Veterinary medicine; Medicine; Virology; Biology; Outbreak; Ecology; Infectious disease (medical specialty); Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0004856731,0.0004827402,0.0004834729,0.000640374,0.0006683544,0.0009205083,0.0002875904,0.0004617898,0.0006863702],"category_scores_gemma":[0.001101379,0.0003908955,0.000447834,0.0006701726,0.0006474408,0.0005251567,0.0007835911,0.0003402596,0.0001692275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006944838,"about_ca_system_score_gemma":0.0007287394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04518124,"about_ca_topic_score_gemma":0.07054767,"domain_scores_codex":[0.9993258,0.0001073603,0.00004910002,0.0001941749,0.0001869498,0.0001367059],"domain_scores_gemma":[0.9995142,0.00006553447,0.0001709744,0.00003532004,0.000174678,0.00003932333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001150522,0.00006848748,0.924044,0.0002616098,0.00009183825,0.001171875,0.004664679,0.0003006514,0.05168089,0.0002022179,0.0005708877,0.01682777],"study_design_scores_gemma":[0.000008150305,0.0002045479,0.9783217,0.0002229039,0.0000791565,0.001668831,0.006399405,0.0005952317,0.007910406,0.0002117025,0.004338025,0.00003993668],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950544,0.0007558276,0.000960173,0.0001584562,0.00001314145,0.00005338061,0.0007688703,0.00002411584,0.002211705],"genre_scores_gemma":[0.9976839,0.0004882267,0.0009343398,0.0001121993,0.000006029467,0.00002512142,0.0004146814,0.000008609027,0.0003267749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04518124,"threshold_uncertainty_score":0.08983654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05385898779009011,"score_gpt":0.3237557199331061,"score_spread":0.269896732143016,"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."}}