{"id":"W4220716362","doi":"10.21203/rs.3.rs-1439969/v1","title":"Community Surveillance of Omicron in Ontario: Wastewater-based Epidemiology Comes of Age.","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Health Canada; Ministère de l’Environnement, de la Protection de la nature et des Parcs; Government of Canada; Ontario Genomics; Genome Canada","keywords":"Public health; Environmental health; Neighbourhood (mathematics); Situation awareness; Epidemiological surveillance; Scalability; Wastewater; Business; Coronavirus disease 2019 (COVID-19); Population; Epidemiology; Geography; Computer science; Medicine; Engineering; Environmental engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001050586,0.0003281457,0.0003937415,0.001055604,0.001294843,0.001887191,0.0007438368,0.0005191767,0.002338084],"category_scores_gemma":[0.003536915,0.0002230269,0.0003485413,0.003738177,0.000471726,0.0005655378,0.001062876,0.0004293846,0.0003843422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0181301,"about_ca_system_score_gemma":0.02473093,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.977457,"about_ca_topic_score_gemma":0.988031,"domain_scores_codex":[0.9987025,0.0001689656,0.00005802098,0.000261691,0.0005588059,0.0002499688],"domain_scores_gemma":[0.9970533,0.0002121222,0.0006280583,0.0001852229,0.001471113,0.0004502743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001362663,0.00003882406,0.936237,0.0002614921,0.0001038803,0.0001973571,0.001479734,0.0009834447,0.00192266,0.0004858478,0.01904659,0.03910679],"study_design_scores_gemma":[0.000012368,0.00003151826,0.9808183,0.00007308895,0.00003394631,0.00006537421,0.002019631,0.00168113,0.0003886142,0.0002903126,0.01456962,0.00001618996],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8874658,0.002944281,0.004346103,0.01089142,0.0001491156,0.0003220519,0.06959414,0.0003130503,0.02397404],"genre_scores_gemma":[0.9754473,0.001231235,0.003654457,0.0008428241,0.00005220425,0.0000771754,0.01340629,0.00004373792,0.00524482],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02254301,"threshold_uncertainty_score":0.1315437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2816181698204595,"score_gpt":0.4603790662043786,"score_spread":0.1787608963839191,"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."}}