{"id":"W4393276601","doi":"10.21203/rs.3.rs-4159693/v1","title":"Learning novel SARS-CoV-2 lineages from wastewater sequencing data","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Mitacs; Ministry of Environment; Queen's University; Trent University; University of Ottawa; Public Health Agency; Public Health Agency of Canada; Ministère de l’Environnement, de la Protection de la nature et des Parcs; Northwestern University","keywords":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Wastewater; Sars virus; Biology; Computational biology; Virology; Computer science; Medicine; Environmental science; Infectious disease (medical specialty); Environmental engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001839892,0.001383729,0.000736782,0.001505092,0.0003187107,0.001110949,0.000806685,0.001700952,0.00138063],"category_scores_gemma":[0.006750589,0.0004569834,0.001071277,0.0009209109,0.0003581637,0.001176284,0.0009157434,0.00138484,0.001513595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004249622,"about_ca_system_score_gemma":0.0008362872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003259794,"about_ca_topic_score_gemma":0.005017971,"domain_scores_codex":[0.999042,0.0002301747,0.00005876022,0.0003600722,0.0001664613,0.0001425029],"domain_scores_gemma":[0.9975625,0.001540609,0.000170769,0.0002552895,0.0003400558,0.0001307043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002070578,0.001011434,0.2054327,0.0005671107,0.0005195385,0.001151086,0.0003278373,0.3402608,0.04060309,0.001598893,0.02734056,0.3791164],"study_design_scores_gemma":[0.0000718518,0.0003701371,0.02371469,0.00006091247,0.0001058536,0.000421684,0.0002876351,0.9500355,0.01101489,0.00828568,0.005594178,0.00003703428],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8191462,0.001884093,0.1545996,0.001714483,0.0002826425,0.0001481919,0.01705321,0.002506446,0.002665014],"genre_scores_gemma":[0.8865743,0.0005760507,0.06715427,0.0004138237,0.0002136763,0.0001002138,0.04138889,0.000163429,0.003415272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003259794,"threshold_uncertainty_score":0.009730399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3272777793403889,"score_gpt":0.4667492163911954,"score_spread":0.1394714370508066,"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."}}