{"id":"W3033566280","doi":"10.14745/ccdr.v46i06a05","title":"Application of artificial intelligence to the in silico assessment of antimicrobial resistance and risks to human and animal health presented by priority enteric bacterial pathogens","year":2020,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Food Inspection Agency; University of Lethbridge; Public Health Agency of Canada","funders":"Public Health Agency; Public Health Agency of Canada; Canadian Food Inspection Agency; Ministero dello Sviluppo Economico; University of Lethbridge","keywords":"Salmonella enterica; Antibiotic resistance; Biology; In silico; Virulence; Salmonella; Whole genome sequencing; Genome; Outbreak; Computational biology; Microbiology; Genetics; Virology; Bacteria; Gene; Antibiotics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.001961741,0.0008628162,0.0006791917,0.001568776,0.0004339246,0.001488046,0.0007456451,0.0008168911,0.0011886],"category_scores_gemma":[0.007579192,0.0002999179,0.0007300396,0.0006842069,0.0005716739,0.000402727,0.0006674401,0.0006025663,0.0001317765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001014464,"about_ca_system_score_gemma":0.001244233,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01508234,"about_ca_topic_score_gemma":0.01142326,"domain_scores_codex":[0.9991903,0.0005000278,0.00006104013,0.00008298521,0.0001217023,0.00004397256],"domain_scores_gemma":[0.9936028,0.005707146,0.000233629,0.0001143955,0.0002439066,0.0000981641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001252447,0.0001371301,0.01212926,0.0001102298,0.0001702247,0.0001325596,0.00007017077,0.9638542,0.0008401189,0.001884264,0.000421997,0.02012461],"study_design_scores_gemma":[0.00001090017,0.0000402683,0.0008360047,0.000009915585,0.00002414871,0.00002542494,0.00002658219,0.9952047,0.0002718939,0.003234639,0.0003097953,0.000005746735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6662502,0.001509959,0.3095229,0.002353599,0.0001625455,0.0004191756,0.001278856,0.001560462,0.01694225],"genre_scores_gemma":[0.9188824,0.000408082,0.07879171,0.0002434748,0.00004332684,0.0001116391,0.0006228816,0.00003187618,0.0008645488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9849176,"threshold_uncertainty_score":0.02998906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02529693420224213,"score_gpt":0.3067802955435223,"score_spread":0.2814833613412802,"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."}}