{"id":"W4311816931","doi":"10.1099/mgen.0.000906","title":"Large-scale comparative genomics to refine the organization of the global Salmonella enterica population structure","year":2022,"lang":"en","type":"article","venue":"Microbial Genomics","topic":"Salmonella and Campylobacter epidemiology","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"Canadian Institutes of Health Research; Genome British Columbia; Michael Smith Health Research BC; Genome Canada","keywords":"Serotype; In silico; Polyphyly; Biology; Salmonella enterica; Salmonella; Population; Typing; Genetics; Multiple Loci VNTR Analysis; Genomics; Multilocus sequence typing; Computational biology; Genome; Phylogenetics; Microbiology; Gene; Tandem repeat; Medicine","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":[0.000807319,0.0004996417,0.0004640361,0.001398907,0.0003246466,0.0006168981,0.0003026074,0.0003636423,0.001095303],"category_scores_gemma":[0.0008983921,0.0001872931,0.0006207668,0.001452028,0.0002316763,0.000482948,0.0006336047,0.0007296865,0.0002991468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004885044,"about_ca_system_score_gemma":0.0004271576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001879143,"about_ca_topic_score_gemma":0.004220936,"domain_scores_codex":[0.9997492,0.00006747051,0.00001233,0.0001064502,0.00002927613,0.00003522207],"domain_scores_gemma":[0.9997003,0.0001361248,0.00005836524,0.00004330194,0.00003095863,0.00003095917],"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":[0.0004837473,0.0002302146,0.02686876,0.000743766,0.0005023946,0.0004906104,0.001045446,0.01403355,0.8853668,0.006028424,0.001100339,0.06310593],"study_design_scores_gemma":[0.0001721328,0.001510168,0.6220875,0.0003037274,0.001252764,0.001921123,0.001846236,0.1700465,0.1139391,0.02023407,0.06648225,0.0002043742],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8688056,0.002772574,0.1159917,0.0004057956,0.00005705296,0.0001000848,0.006952091,0.0006366062,0.004278499],"genre_scores_gemma":[0.8978912,0.001257825,0.0882131,0.0002406569,0.00003735977,0.00008154468,0.01147665,0.0001828333,0.0006187524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001879143,"threshold_uncertainty_score":0.00426954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01447060629759142,"score_gpt":0.2220600092011294,"score_spread":0.207589402903538,"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."}}