{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007573088,0.0001695216,0.0002759606,0.00009289213,0.00007207964,0.00001824267,0.0001132925,0.00008385527,0.00003301563],"category_scores_gemma":[0.001698487,0.000136025,0.00003755867,0.0001002192,0.0001046016,0.00009911634,0.0002083681,0.0002411765,0.000141789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009292956,"about_ca_system_score_gemma":0.00002450187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000158702,"about_ca_topic_score_gemma":0.00002177885,"domain_scores_codex":[0.9985319,0.0001076779,0.00033786,0.0006236556,0.0001298684,0.0002689935],"domain_scores_gemma":[0.9986643,0.0006033257,0.00004024142,0.0006163132,0.000003312573,0.00007248582],"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":[0.00006129251,0.0001117041,0.04318001,0.0001101786,0.0000547679,0.000178297,0.0001167676,0.000007578249,0.9397089,0.000006544041,0.001149531,0.01531445],"study_design_scores_gemma":[0.0008713842,0.000755142,0.02464545,0.0005059797,0.0001639987,0.0004569399,0.0001513245,0.04012888,0.8496672,0.00009447182,0.08226779,0.0002914503],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958825,0.0004154527,0.00210674,0.0005367653,0.0002492975,0.0003466516,0.00006171367,0.0001283718,0.0002725832],"genre_scores_gemma":[0.9925206,0.000006019698,0.004401917,0.002739903,0.0001256696,0.00002891091,0.00006081218,0.00002671024,0.00008949728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09004168,"threshold_uncertainty_score":0.5546937,"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."}}