{"id":"W4396864602","doi":"10.3390/environments11050100","title":"Integrating Wastewater-Based Epidemiology and Mobility Data to Predict SARS-CoV-2 Cases","year":2024,"lang":"en","type":"article","venue":"Environments","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Univariate; Pandemic; Multivariate statistics; Epidemiology; Coronavirus disease 2019 (COVID-19); Public health; Multivariate analysis; Econometrics; Computer science; Data science; Environmental health; Data mining; Statistics; Medicine; Economics; Mathematics; Nursing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002392517,0.0008767741,0.0005738679,0.001043431,0.0001969637,0.001004868,0.0005332495,0.0006236426,0.0007579268],"category_scores_gemma":[0.005307672,0.0003845807,0.001279847,0.0006963056,0.0003087829,0.0009509043,0.0009804519,0.0007872843,0.0002181928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006696871,"about_ca_system_score_gemma":0.001108582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02184397,"about_ca_topic_score_gemma":0.01862659,"domain_scores_codex":[0.9991254,0.0004104062,0.0000558675,0.0001995665,0.00008451,0.0001242092],"domain_scores_gemma":[0.9981592,0.001033487,0.0003329327,0.0001505042,0.0001821803,0.0001416502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003190554,0.0002819654,0.5537422,0.00003191482,0.0002192646,0.0001344283,0.00007205758,0.4296205,0.0008277388,0.0007123679,0.0003630619,0.01367537],"study_design_scores_gemma":[0.00001439062,0.0002516256,0.07671458,0.00001477088,0.00005988657,0.00005499444,0.0001591361,0.9209267,0.0004956803,0.0009865002,0.0002981592,0.00002359152],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9883958,0.00009498031,0.01000231,0.0002611151,0.00001430542,0.00003218109,0.000673924,0.00005981901,0.0004656535],"genre_scores_gemma":[0.9968418,0.0000443241,0.002257216,0.00001441476,0.00000627804,0.000008744429,0.0005972614,0.000003652124,0.0002262309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02184397,"threshold_uncertainty_score":0.04343367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1524954672106256,"score_gpt":0.3709868548136182,"score_spread":0.2184913876029926,"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."}}