{"id":"W3213712254","doi":"10.1016/j.scitotenv.2021.151434","title":"Validating and optimizing the method for molecular detection and quantification of SARS-CoV-2 in wastewater","year":2021,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Provincial Laboratory of Public Health; University of Alberta","funders":"University of Alberta","keywords":"Wastewater; Chromatography; Real-time polymerase chain reaction; Extraction (chemistry); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Chemistry; Population; Coronavirus disease 2019 (COVID-19); Solid phase extraction; Virology; Biology; Microbiology; Medicine; Environmental science; Environmental engineering; Biochemistry; Gene; Internal medicine; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001197415,0.00005504326,0.00009335672,0.00003202188,0.0001424487,0.0000149227,0.00007715706,0.00001752726,0.000001091967],"category_scores_gemma":[0.0002361625,0.00002882243,0.00003199439,0.0001661914,0.0003276333,0.00004899237,0.0001188259,0.00007055141,3.034408e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003209498,"about_ca_system_score_gemma":0.00001641952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004092898,"about_ca_topic_score_gemma":0.000001220926,"domain_scores_codex":[0.9993083,0.00007299838,0.0001652978,0.0001614846,0.0001915659,0.0001003857],"domain_scores_gemma":[0.9995183,0.0001094044,0.0001062729,0.0002426565,0.00001507188,0.000008288112],"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":[0.00001295202,0.00001388256,0.00001123001,0.0000157409,0.00000487322,2.55525e-7,0.000474327,0.001534548,0.9940875,0.00005297607,1.624978e-7,0.003791588],"study_design_scores_gemma":[0.0002210855,0.00004305308,0.001222418,0.0000376381,0.00003123269,0.0000737997,0.0006378673,0.05574835,0.9417067,0.0002423524,0.000005552295,0.00003001702],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908957,0.0000960441,0.007970402,0.0006533657,0.00004617077,0.0002617292,6.917807e-7,0.000003338325,0.00007248893],"genre_scores_gemma":[0.9894696,0.000005143638,0.01043645,0.00005099577,0.000006868489,0.0000115373,1.04538e-7,0.000004680859,0.00001463645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05421381,"threshold_uncertainty_score":0.1207179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04693757648056894,"score_gpt":0.3081471927374513,"score_spread":0.2612096162568823,"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."}}