{"id":"W4411520230","doi":"10.1016/j.watres.2025.124071","title":"Passive sampling for genomic surveillance of SARS-CoV-2 in wastewater resource recovery facility: Insights for pandemic preparedness","year":2025,"lang":"en","type":"article","venue":"Water Research","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Windsor; Children's Hospital of Eastern Ontario; University of Ottawa","funders":"Ministry of Environment; Canadian Institutes of Health Research; University of Ottawa","keywords":"Preparedness; Pandemic; Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Wastewater; Sampling (signal processing); 2019-20 coronavirus outbreak; Environmental science; Resource recovery; Waste management; Engineering; Environmental engineering; Virology; Medicine; Telecommunications; Political science; Outbreak; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001054254,0.0006326938,0.0005757726,0.0007667409,0.0005471994,0.00133381,0.0006077269,0.00073331,0.001117436],"category_scores_gemma":[0.00112381,0.0002282924,0.0004488463,0.0007281569,0.0006503828,0.0008780968,0.0006435012,0.0006289854,0.0002504789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001315868,"about_ca_system_score_gemma":0.002488195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02669239,"about_ca_topic_score_gemma":0.0609278,"domain_scores_codex":[0.9990506,0.0002105288,0.00004602094,0.0002266864,0.0002683816,0.0001979677],"domain_scores_gemma":[0.9992365,0.0001581779,0.000178501,0.00005166463,0.0002943559,0.00008081989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004291984,0.0003580339,0.3173366,0.001249625,0.00009567628,0.0004878952,0.001511787,0.003102492,0.5371742,0.001151121,0.001862634,0.1352408],"study_design_scores_gemma":[0.00004784034,0.002129675,0.7459225,0.000654244,0.0003862234,0.0009990205,0.01356538,0.0230944,0.1807743,0.0026411,0.02957462,0.0002106793],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9608336,0.003669297,0.0276212,0.002018729,0.00008199897,0.0002372246,0.001499344,0.0001688671,0.003869722],"genre_scores_gemma":[0.9536331,0.002652924,0.03904955,0.0008592692,0.00004129252,0.0001476683,0.001784975,0.00004124828,0.001789919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02669239,"threshold_uncertainty_score":0.05307406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1846035442984094,"score_gpt":0.4136778158293243,"score_spread":0.2290742715309149,"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."}}