{"id":"W4210526381","doi":"10.1016/j.jes.2022.01.039","title":"Wastewater Based Surveillance of SARS-CoV-2: Challenges and Perspective from a Canadian Inter-laboratory Study","year":2022,"lang":"en","type":"editorial","venue":"Journal of Environmental Sciences","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Wastewater; Perspective (graphical); Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Environmental science; Sars virus; Computer science; Virology; Environmental engineering; Medicine; Outbreak; Infectious disease (medical specialty); Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001153125,0.0002125537,0.0005468273,0.0003987383,0.0001569629,0.00003032007,0.0002541032,0.0001278727,0.0001250178],"category_scores_gemma":[0.0004378342,0.0001682142,0.0001003955,0.0001413924,0.0004137309,0.0001330812,0.00008327045,0.0006550713,0.000002170555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007156375,"about_ca_system_score_gemma":0.0005781889,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01072722,"about_ca_topic_score_gemma":0.01583195,"domain_scores_codex":[0.9976766,0.0002161733,0.0004610111,0.0003594306,0.001066403,0.0002204273],"domain_scores_gemma":[0.9987003,0.0004074616,0.0005806471,0.0001544366,0.00005442478,0.0001027227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001329945,0.003625256,0.1170028,0.0002039927,0.001596498,0.002063059,0.02783524,0.00001711379,0.7223204,0.000005082659,0.1146165,0.009384068],"study_design_scores_gemma":[0.01438412,0.03312046,0.1165422,0.001115191,0.0008528382,0.0002235018,0.1967939,0.0001997332,0.141928,0.0001593387,0.4930416,0.00163908],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9253013,0.003321184,3.680316e-7,0.0001773403,0.07041223,0.0001810151,0.0001496155,0.000005062007,0.0004518659],"genre_scores_gemma":[0.9649359,0.0002006227,0.00009566618,0.0001009388,0.03463387,0.000003883603,0.000002764533,0.00001795972,0.000008418819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5803924,"threshold_uncertainty_score":0.9958605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02976375068633986,"score_gpt":0.290921250324273,"score_spread":0.2611574996379331,"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."}}