{"id":"W3121089739","doi":"10.1371/journal.pone.0248893","title":"Placing sensors in sewer networks: A system to pinpoint new cases of coronavirus","year":2021,"lang":"en","type":"article","venue":"PLoS ONE","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; York University","funders":"Natural Sciences and Engineering Research Council of Canada; Massachusetts Department of Transportation; Yale University; U.S. Department of Transportation","keywords":"Wireless sensor network; Computer science; Heuristics; Pipeline (software); Tree (set theory); Tree network; Genetic algorithm; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Real-time computing; Artificial intelligence; Coronavirus disease 2019 (COVID-19); Algorithm; Machine learning; Mathematics; Computer network; Medicine","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":[0.0001191411,0.00009616065,0.0003500787,0.00009964007,0.00002350099,0.000009319959,0.0000278569,0.00005663255,0.00003342443],"category_scores_gemma":[0.0007525673,0.00009773738,0.00004232897,0.0004128993,0.00001070334,0.0000282196,0.00003676758,0.0001548336,0.0000363824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001293068,"about_ca_system_score_gemma":0.0001017719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005638688,"about_ca_topic_score_gemma":0.0002453057,"domain_scores_codex":[0.9990579,0.00004405334,0.0002968518,0.0001974589,0.0002187261,0.0001850257],"domain_scores_gemma":[0.999339,0.0001895764,0.0000641709,0.0002048515,0.000117477,0.00008485912],"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":[0.0003734991,0.0008697394,0.04197704,0.0009292324,0.0002570894,0.002751149,0.001083295,0.0006449777,0.9467571,0.0000651934,0.0001379099,0.004153777],"study_design_scores_gemma":[0.001547466,0.0002597209,0.005305037,0.005332757,0.0001655943,0.0006064354,0.001222918,0.01321924,0.9720357,0.000003097625,0.0001406948,0.0001613114],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972748,0.0003484059,0.0001536468,0.0001351839,0.00007888064,0.0002353604,0.000001995663,0.00009899647,0.001672696],"genre_scores_gemma":[0.9962873,0.000004077546,0.002549665,0.0005998914,0.0002062482,0.000008783471,0.00000215946,0.00002097233,0.0003209097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03667201,"threshold_uncertainty_score":0.3985614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1131281976621723,"score_gpt":0.2944742054940876,"score_spread":0.1813460078319153,"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."}}