{"id":"W2983439800","doi":"10.1016/j.ygeno.2019.10.022","title":"Selective whole genome amplification and sequencing of Coxiella burnetii directly from environmental samples","year":2019,"lang":"en","type":"article","venue":"Genomics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; Laurentian University","funders":"National Institute on Minority Health and Health Disparities; National Institutes of Health","keywords":"Biology; Coxiella burnetii; Genome; Metagenomics; DNA sequencing; Computational biology; Whole genome sequencing; Genetics; Genomics; Multiple displacement amplification; Population; Gene; Polymerase chain reaction; DNA extraction; Microbiology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006906578,0.0001196295,0.000161356,0.00002043176,0.00004838542,0.00001073118,0.0000984761,0.00007872594,0.00001222269],"category_scores_gemma":[0.000007589845,0.0001264817,0.0000414568,0.00002260017,0.00006899269,7.663753e-7,0.0001193918,0.00004120762,0.0000120883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003369716,"about_ca_system_score_gemma":0.00004237537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009621176,"about_ca_topic_score_gemma":0.0000208427,"domain_scores_codex":[0.9993112,0.00002240016,0.0001690043,0.0003050785,0.00005259142,0.0001397397],"domain_scores_gemma":[0.9995984,0.00001758022,0.00009796524,0.0002286183,0.00001847805,0.00003890315],"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":[0.00002838856,0.00001186705,0.01636767,0.000006559637,0.00007604752,1.038506e-7,0.0004147005,0.0002150401,0.9821686,0.00002542213,0.00001519453,0.0006704628],"study_design_scores_gemma":[0.0005444793,0.0002519112,0.2018293,0.000005679211,0.00003897454,0.000004232311,0.0008586043,0.0001041643,0.7604637,0.0005947278,0.03500508,0.0002991178],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948049,0.003956482,0.0001776006,0.00002008999,0.00006007387,0.0001901222,0.0003561367,0.000001913011,0.000432702],"genre_scores_gemma":[0.9966363,0.001249592,0.001553341,0.00005752131,0.00008656108,0.000006526823,0.0002250052,0.0000181085,0.0001670481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2217049,"threshold_uncertainty_score":0.5157773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009955990602397992,"score_gpt":0.1946683606831702,"score_spread":0.1847123700807722,"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."}}