{"id":"W3033763368","doi":"10.1128/msphere.00293-20","title":"Combining Whole-Genome Sequencing and Multimodel Phenotyping To Identify Genetic Predictors of <i>Salmonella</i> Virulence","year":2020,"lang":"en","type":"article","venue":"mSphere","topic":"Salmonella and Campylobacter epidemiology","field":"Agricultural and Biological Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Food Inspection Agency; Fonds de Recherche du Québec – Nature et Technologies; University of Guelph; Université Laval; Université de Montréal; Dalhousie University; Jewish General Hospital; McGill University","funders":"Genome Canada","keywords":"Virulence; Salmonella; Salmonella enterica; Biology; Serotype; Genomics; Whole genome sequencing; Genome; Food safety; Genetic diversity; Genetics; Microbiology; Bacteria; Gene; Population; Medicine; Food science; Environmental health","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":[0.0009383873,0.0006028161,0.0005583389,0.0009680018,0.0002198779,0.0006062826,0.0002483918,0.0003711966,0.0004977781],"category_scores_gemma":[0.0009651801,0.0001970416,0.0006428129,0.0009489245,0.0001804645,0.0003693528,0.0005678307,0.0005210417,0.000196302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002508537,"about_ca_system_score_gemma":0.0002820034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00231308,"about_ca_topic_score_gemma":0.00482435,"domain_scores_codex":[0.9996015,0.0001232427,0.00002113271,0.0001564047,0.0000518436,0.00004595235],"domain_scores_gemma":[0.9994041,0.0002353145,0.0001188677,0.0001006889,0.00006760911,0.00007346617],"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":[0.0004847994,0.0004297894,0.2742398,0.0001528445,0.0008093467,0.0001811222,0.0003273784,0.01435317,0.648057,0.0004478929,0.0003453883,0.06017148],"study_design_scores_gemma":[0.00002712573,0.0005636559,0.8345273,0.00003329929,0.0004043008,0.0004402582,0.0004400025,0.1077701,0.05177041,0.00120593,0.002755089,0.00006259936],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9669673,0.0001648359,0.03067017,0.00006843908,0.0000110729,0.00004907731,0.001316184,0.0001183741,0.0006346178],"genre_scores_gemma":[0.9452725,0.0001768713,0.05097872,0.0001243588,0.000009764636,0.00004652223,0.00283061,0.00005448599,0.0005060699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00231308,"threshold_uncertainty_score":0.004962742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04087016517097351,"score_gpt":0.2481947387035568,"score_spread":0.2073245735325833,"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."}}