{"id":"W4402751636","doi":"10.3390/v16091495","title":"Combining Short- and Long-Read Sequencing Technologies to Identify SARS-CoV-2 Variants in Wastewater","year":2024,"lang":"en","type":"article","venue":"Viruses","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Toronto Metropolitan University; Université Laval; Université de Montréal; Montreal Heart Institute; Polytechnique Montréal; McGill Genome Centre; Canada's Michael Smith Genome Sciences Centre; McGill University; University of British Columbia","funders":"Canadian Institutes of Health Research; Canada Foundation for Innovation; McGill University","keywords":"Biology; Genetics; DNA sequencing; Amplicon sequencing; Lineage (genetic); Population; Nanopore sequencing; Amplicon; Mutation rate; Deep sequencing; Computational biology; Evolutionary biology; Genome; Gene; Polymerase chain reaction; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0009964677,0.0007187754,0.0004274134,0.00081038,0.0007417664,0.001064711,0.000470559,0.0006881993,0.0008181316],"category_scores_gemma":[0.001197022,0.0002437612,0.0003861065,0.0008301791,0.0003807879,0.0003359806,0.0004451355,0.0003524008,0.0005409688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001210397,"about_ca_system_score_gemma":0.001453893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06671055,"about_ca_topic_score_gemma":0.2081292,"domain_scores_codex":[0.9984298,0.0002441856,0.00008269682,0.0004668562,0.0006472817,0.0001291694],"domain_scores_gemma":[0.9992697,0.0001750599,0.00009963822,0.00005157021,0.0003435733,0.00006052662],"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":[0.0004064439,0.0001654363,0.05589506,0.0003305021,0.0001608915,0.000192494,0.0005854877,0.001882152,0.8962591,0.0001900156,0.0005655414,0.04336689],"study_design_scores_gemma":[0.000042735,0.0009147953,0.2398932,0.0001227216,0.0002971972,0.0007777648,0.001205263,0.03369932,0.7085264,0.0003718767,0.01398135,0.0001672754],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9451559,0.001355215,0.04615119,0.0002065713,0.00006224101,0.0003201173,0.003173145,0.0006999457,0.002875684],"genre_scores_gemma":[0.8496513,0.0009833785,0.1403931,0.0003853498,0.00002805707,0.0002713925,0.003441312,0.0001116756,0.004734472],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06671055,"threshold_uncertainty_score":0.1326445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.114693092547822,"score_gpt":0.3781791607205546,"score_spread":0.2634860681727325,"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."}}