{"id":"W4391556212","doi":"10.1128/jcm.00103-22","title":"Rapid, high-throughput, cost-effective whole-genome sequencing of SARS-CoV-2 using a condensed library preparation of the Illumina DNA Prep kit","year":2024,"lang":"en","type":"article","venue":"Journal of Clinical Microbiology","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Centre for Disease Control","funders":"British Columbia Centre for Disease Control","keywords":"Turnaround time; Illumina dye sequencing; DNA sequencing; Workflow; Consumables; Whole genome sequencing; Computational biology; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Throughput; Protocol (science); Computer science; Coronavirus disease 2019 (COVID-19); Genome; Biology; DNA; Medicine; Chemistry; Genetics; Gene; Operating system; Database","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":[0.001155172,0.0009835974,0.0007773216,0.0008524899,0.0006093276,0.001324895,0.0006675243,0.0006126416,0.005550707],"category_scores_gemma":[0.0009038929,0.0007654976,0.001207841,0.0007431871,0.0004333078,0.0004957261,0.001097542,0.001356213,0.005418794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005517274,"about_ca_system_score_gemma":0.001531065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001745028,"about_ca_topic_score_gemma":0.00763078,"domain_scores_codex":[0.9985101,0.0002117249,0.00007815837,0.0004279139,0.0006423797,0.0001296753],"domain_scores_gemma":[0.9995566,0.00009300697,0.00006061621,0.00009416445,0.0001466087,0.00004905523],"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":[0.0001205693,0.00008575086,0.0008574644,0.0003637905,0.00006438564,0.00005056238,0.00007971141,0.000563366,0.9619564,0.0006105163,0.003949327,0.03129824],"study_design_scores_gemma":[0.00006187939,0.0004847948,0.009758661,0.00008182659,0.0001087543,0.0003856453,0.00006712973,0.006151049,0.8966777,0.0006396319,0.0854883,0.00009466901],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1793015,0.003783809,0.7656947,0.0008680566,0.0005134748,0.001950263,0.01842791,0.008103761,0.02135647],"genre_scores_gemma":[0.1404336,0.003211674,0.7810262,0.000978235,0.0001481785,0.002247352,0.05013447,0.001271117,0.02054913],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005550707,"threshold_uncertainty_score":0.01856899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1239903391675301,"score_gpt":0.4400544160763655,"score_spread":0.3160640769088354,"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."}}