{"id":"W4200055356","doi":"10.2166/wh.2021.186","title":"The devil is in the details: emerging insights on the relevance of wastewater surveillance for SARS-CoV-2 to public health","year":2021,"lang":"en","type":"article","venue":"Journal of Water and Health","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Water Network; University of Waterloo; University of Alberta","funders":"","keywords":"Pandemic; Public health; Environmental health; Relevance (law); Coronavirus disease 2019 (COVID-19); Public health surveillance; Business; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Health care; Medicine; Political science; Disease; Infectious disease (medical specialty); Pathology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003376005,0.000103541,0.0003376659,0.00009628925,0.0003202814,0.00005406022,0.0001215344,0.0000286973,0.000001891251],"category_scores_gemma":[0.000307191,0.00003963133,0.00008370481,0.0002148337,0.00003514951,0.00006245696,0.00002540524,0.0003163731,0.00000153504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008325972,"about_ca_system_score_gemma":0.0003010117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007915093,"about_ca_topic_score_gemma":0.0007264102,"domain_scores_codex":[0.9982856,0.0002840215,0.0006714991,0.0001289199,0.0002878831,0.0003420951],"domain_scores_gemma":[0.9988002,0.0004073065,0.0002683864,0.0002152681,0.0002485843,0.00006022497],"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":[0.002368078,0.001239879,0.04918766,0.003026973,0.0005743169,0.0001841193,0.2348225,0.00002587519,0.3888835,0.003118738,0.06193671,0.2546316],"study_design_scores_gemma":[0.002882324,0.002729932,0.01651104,0.0009932998,0.00001621382,0.0008954653,0.007152823,0.0003342302,0.487971,0.001565108,0.4787641,0.0001845012],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7860327,0.001635382,0.0001331781,0.2116848,0.0001736795,0.0002566348,0.000001509109,0.000003797009,0.00007840207],"genre_scores_gemma":[0.9503539,0.0005314942,0.0002449825,0.04865012,0.000170576,0.000006971142,7.256282e-7,0.00001169491,0.00002950432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4168273,"threshold_uncertainty_score":0.2463378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1290546330786864,"score_gpt":0.3712686550033983,"score_spread":0.2422140219247119,"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."}}