{"id":"W3189596501","doi":"10.1016/j.scs.2021.103226","title":"Removal of SARS-CoV-2 using UV+Filter in built environment","year":2021,"lang":"en","type":"article","venue":"Sustainable Cities and Society","topic":"Infection Control and Ventilation","field":"Medicine","cited_by":71,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Aerosolization; Aerosol; Filtration (mathematics); Filter (signal processing); Environmental science; Ultraviolet; Air filter; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Bioaerosol; Electrostatic precipitator; HEPA; Materials science; Process engineering; Coronavirus disease 2019 (COVID-19); Pulp and paper industry; Waste management; Computer science; Meteorology; Optoelectronics; Engineering; Physics; Mechanical engineering; Medicine","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":[0.0001590734,0.0002507102,0.0003466356,0.0001565104,0.0003534799,0.000480178,0.0002266845,0.0003649818,0.001513357],"category_scores_gemma":[0.0001721244,0.0001287516,0.0005332634,0.0001434655,0.0001200087,0.000238191,0.0002165567,0.000299515,0.0003418926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001777733,"about_ca_system_score_gemma":0.0002778057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00307494,"about_ca_topic_score_gemma":0.005569862,"domain_scores_codex":[0.9997638,0.00002859019,0.000009611089,0.00005069924,0.00007485327,0.00007246564],"domain_scores_gemma":[0.9998982,0.00002069136,0.00002030529,0.00001063237,0.00003776158,0.0000124482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003128801,0.0001425213,0.002099514,0.0001676219,0.00002066965,0.00005257452,0.00007429732,0.0003922198,0.9871705,0.00005390209,0.000172964,0.009340256],"study_design_scores_gemma":[0.0000107768,0.001157613,0.01156003,0.00001723826,0.00003738111,0.00009715444,0.0001741042,0.001842056,0.9827167,0.00004130643,0.002331058,0.00001467013],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957215,0.0008234784,0.001844253,0.00004340946,0.00004099203,0.00001867204,0.0001134597,0.00003969452,0.001354563],"genre_scores_gemma":[0.9901652,0.0006080636,0.004173299,0.0001045223,0.00001363645,0.00002096816,0.0002265438,0.00001617214,0.004671601],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00307494,"threshold_uncertainty_score":0.006114066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02499505442242365,"score_gpt":0.278393715968898,"score_spread":0.2533986615464743,"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."}}