{"id":"W3195766053","doi":"10.1371/journal.pcbi.1009264","title":"Mitigating COVID-19 outbreaks in workplaces and schools by hybrid telecommuting","year":2021,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministère de l'Enseignement Supérieur, de la Recherche, de la Science et de la Technologie; Fondation de France; Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation; Agence Nationale de la Recherche; Université Paris-Saclay; Pfizer","keywords":"Telecommuting; Coronavirus disease 2019 (COVID-19); Outbreak; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Geography; Pandemic; Business; Biology; Virology; Engineering; Medicine; Work (physics)","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":[0.0009233869,0.0007051518,0.0006450613,0.0007160897,0.0005945468,0.00125464,0.001278304,0.0008615194,0.001442351],"category_scores_gemma":[0.002478254,0.0002219907,0.0006478711,0.0004574066,0.0005591042,0.001026996,0.001307339,0.0004173259,0.000214398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007179347,"about_ca_system_score_gemma":0.0007312305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007551593,"about_ca_topic_score_gemma":0.007480637,"domain_scores_codex":[0.9993771,0.0002552487,0.00001671876,0.00008607529,0.00006002134,0.0002049554],"domain_scores_gemma":[0.9985397,0.0006262455,0.0003714203,0.0001163849,0.0001134047,0.0002328872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003339746,0.0004936389,0.02515093,0.00008120041,0.0001222275,0.0004908486,0.0002222534,0.9418975,0.004199076,0.004826538,0.00083298,0.0213489],"study_design_scores_gemma":[0.00006717642,0.0004988413,0.006104916,0.00001444704,0.00005403783,0.000113692,0.0004541106,0.9870955,0.001074761,0.003815725,0.0006852564,0.00002142551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9461955,0.0001725655,0.04948715,0.0002714384,0.00002903355,0.00007735108,0.0001082704,0.00017098,0.003487747],"genre_scores_gemma":[0.9962608,0.00006875853,0.003128061,0.00002630095,0.000007742864,0.00002391865,0.00004848668,0.000006503036,0.0004293185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007551593,"threshold_uncertainty_score":0.01501524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1627907082468546,"score_gpt":0.4131288612440854,"score_spread":0.2503381529972308,"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."}}