{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002686321,0.0005844875,0.0005048513,0.0006767188,0.0004511909,0.0005073274,0.0003686098,0.0005285864,0.0008532387],"category_scores_gemma":[0.0009487672,0.0004544084,0.0004409435,0.0006618552,0.0004011463,0.0002012698,0.000506367,0.0007356759,0.001000279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002304896,"about_ca_system_score_gemma":0.0007979532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005616989,"about_ca_topic_score_gemma":0.01679956,"domain_scores_codex":[0.9995123,0.00004211064,0.00001896336,0.000169601,0.0001536322,0.0001034231],"domain_scores_gemma":[0.9995043,0.0001543957,0.00005694981,0.00006396631,0.0001782976,0.00004213219],"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":[0.00008091996,0.00002383198,0.002040141,0.00005501358,0.00001072307,0.00003910049,0.0001054562,0.00007980267,0.9947159,0.00003951276,0.000125557,0.002684138],"study_design_scores_gemma":[0.00004357804,0.0003276477,0.1664708,0.0000566312,0.0001752499,0.000609822,0.0005989762,0.002268474,0.8143999,0.0002872815,0.01473448,0.00002709336],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9197519,0.001392655,0.05579605,0.0003245562,0.0001019634,0.0004378599,0.01654392,0.000462432,0.00518865],"genre_scores_gemma":[0.858864,0.001650104,0.0824473,0.0008616034,0.00008853297,0.0006377557,0.04476948,0.0004240502,0.01025719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005616989,"threshold_uncertainty_score":0.0111686,"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."}}