{"id":"W4280538175","doi":"10.1101/2022.04.25.489472","title":"Targeted genomic sequencing with probe capture for discovery and surveillance of coronaviruses in bats","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina; Vancouver Island University; BC Centre for Disease Control; CARE Canada; University of British Columbia","funders":"Genome Prairie; Saskatchewan Health Research Foundation; United States Agency for International Development","keywords":"Biology; Genome; Amplicon; Genomics; Coronavirus; Computational biology; Deep sequencing; DNA sequencing; Whole genome sequencing; Phylogenetic tree; Genetics; Virology; Evolutionary biology; Polymerase chain reaction; Coronavirus disease 2019 (COVID-19); Gene; Infectious disease (medical specialty)","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":[0.0009777788,0.0006272221,0.0004456071,0.0005051257,0.0002672822,0.0006459345,0.0003273599,0.0007794526,0.0008843046],"category_scores_gemma":[0.0008616151,0.000342338,0.0003327813,0.0004325541,0.0005125673,0.0004217061,0.0006764885,0.0004961253,0.0005968047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003889391,"about_ca_system_score_gemma":0.0002908796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001208809,"about_ca_topic_score_gemma":0.002748266,"domain_scores_codex":[0.9993875,0.0001832225,0.00002544524,0.000206769,0.0001302264,0.00006677111],"domain_scores_gemma":[0.999726,0.0001172587,0.00005033279,0.00004220605,0.0000382096,0.00002602514],"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":[0.00005503645,0.00001095163,0.002297725,0.00004312731,0.00001602983,0.00003226407,0.00004579513,0.0006191152,0.9922259,0.0001108016,0.0001570791,0.004386208],"study_design_scores_gemma":[0.00002418035,0.0005109734,0.03382451,0.00004290287,0.00009728281,0.0008205762,0.0002519477,0.02497424,0.9265511,0.0007548011,0.0121009,0.00004655881],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8263747,0.002486317,0.162634,0.0005591557,0.00008177851,0.0002026239,0.003490008,0.001452364,0.002719123],"genre_scores_gemma":[0.7958373,0.00149623,0.193016,0.0008334223,0.00004828129,0.0002765731,0.005379112,0.0002530053,0.002860024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001208809,"threshold_uncertainty_score":0.005171061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02933130781071133,"score_gpt":0.2768698487759879,"score_spread":0.2475385409652766,"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."}}