{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000182512,0.0001258738,0.0002087006,0.000005530754,0.0005198402,0.00002183115,0.0005352636,0.00005580171,0.0002954165],"category_scores_gemma":[0.00002007333,0.00004699302,0.00007740517,0.0005559759,0.00004070324,0.00001986924,0.0005029221,0.0001299842,0.000007750094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002408546,"about_ca_system_score_gemma":0.00001587304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003467271,"about_ca_topic_score_gemma":0.00129678,"domain_scores_codex":[0.9988801,0.0002721586,0.0002919759,0.0002416444,0.00009369914,0.000220402],"domain_scores_gemma":[0.9995267,0.0000518614,0.0002015716,0.0001215733,0.00005880539,0.00003946653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005537799,0.00004993214,0.09346684,0.000001764576,0.00001698047,2.391643e-7,0.001237888,0.002910917,0.8993273,0.0001742757,0.00220555,0.000552942],"study_design_scores_gemma":[0.0001891711,0.0001383206,0.8778788,0.000002669,0.00003156388,0.00004131078,0.001765692,0.0001255821,0.01031477,0.0005157309,0.1087917,0.0002046682],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963028,0.0000415338,0.00002955885,0.001771746,0.0004201822,0.0003219897,0.00103517,0.00001657496,0.00006038367],"genre_scores_gemma":[0.9977142,0.000006231255,0.0001262952,0.001561651,0.0001815424,0.000003851131,0.0003427776,0.00000168952,0.0000617652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8890125,"threshold_uncertainty_score":0.3998243,"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."}}