{"id":"W3183766476","doi":"10.1016/j.diagmicrobio.2021.115508","title":"Target capture sequencing of SARS-CoV-2 genomes using the ONETest Coronaviruses Plus","year":2021,"lang":"en","type":"article","venue":"Diagnostic Microbiology and Infectious Disease","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fusion Genomics (Canada)","funders":"","keywords":"Amplicon; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Amplicon sequencing; Biology; Sequence (biology); Genome; Coronavirus disease 2019 (COVID-19); Computational biology; Whole genome sequencing; DNA sequencing; 2019-20 coronavirus outbreak; Virology; Coronavirus; Sequence analysis; Genetics; Polymerase chain reaction; Gene; Medicine; 16S ribosomal RNA; Infectious disease (medical specialty)","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":[0.0008231096,0.0005515034,0.0006024779,0.0005997842,0.0004429729,0.0008066365,0.0004897367,0.0005196056,0.001701402],"category_scores_gemma":[0.001100001,0.0004678435,0.0008399188,0.0003908274,0.0002865463,0.0004687472,0.0008974864,0.0005111351,0.0008227367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002692303,"about_ca_system_score_gemma":0.0005466455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001567344,"about_ca_topic_score_gemma":0.007195824,"domain_scores_codex":[0.999148,0.0001221348,0.00005335867,0.0002638363,0.0003058293,0.0001067965],"domain_scores_gemma":[0.9994944,0.0001379616,0.00009339796,0.00008822525,0.000133518,0.00005236503],"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":[0.0002292805,0.00009407832,0.006526084,0.0001475909,0.00005817362,0.00008085647,0.0001912437,0.001450506,0.9672502,0.0004119951,0.0006487001,0.02291123],"study_design_scores_gemma":[0.00006671937,0.001314912,0.03056518,0.00005002906,0.0001009316,0.0008819401,0.0001790038,0.02625239,0.9213538,0.000470068,0.0186505,0.0001145231],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7892308,0.0005514714,0.1939391,0.0001462368,0.00009297582,0.000722828,0.006452989,0.001552906,0.007310894],"genre_scores_gemma":[0.5831924,0.0006823337,0.3755824,0.0007419827,0.00006123635,0.001009078,0.02916913,0.0007159204,0.0088455],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001701402,"threshold_uncertainty_score":0.005691826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02101012745436762,"score_gpt":0.2561322385257415,"score_spread":0.2351221110713738,"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."}}