{"id":"W4386989428","doi":"10.1016/j.mimet.2023.106827","title":"Rapid identification of Salmonella serovars Enteritidis and Typhimurium using whole cell matrix assisted laser desorption ionization – Time of flight mass spectrometry (MALDI-TOF MS) coupled with multivariate analysis and artificial intelligence","year":2023,"lang":"en","type":"article","venue":"Journal of Microbiological Methods","topic":"Bacterial Identification and Susceptibility Testing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Society for Anthropological Sciences; Ontario Ministry of Agriculture, Food and Rural Affairs; Ontario Agri-Food Innovation Alliance; University of Guelph; SAS Institute","keywords":"Salmonella enteritidis; Principal component analysis; Serotype; Salmonella; Matrix-assisted laser desorption/ionization; Mass spectrometry; Multivariate statistics; Artificial intelligence; Chromatography; Chemistry; Biology; Computer science; Microbiology; Machine learning; Desorption; Bacteria; Genetics","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":[0.0004525544,0.0006119404,0.0004272668,0.0008115178,0.0001822283,0.0005386403,0.0002763705,0.0005119613,0.000445612],"category_scores_gemma":[0.000715677,0.0002632898,0.0003744008,0.0003405928,0.0002230201,0.0006163583,0.0004629777,0.0005351112,0.0005066372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001688165,"about_ca_system_score_gemma":0.0003262821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006535059,"about_ca_topic_score_gemma":0.001467827,"domain_scores_codex":[0.9995727,0.00005286188,0.00003800859,0.00009066386,0.0002070106,0.00003874553],"domain_scores_gemma":[0.9995521,0.00009057574,0.00008246496,0.00002617148,0.0002008903,0.00004781868],"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":[0.00009108265,0.00002949152,0.000777418,0.00002953859,0.000007362839,0.00002308877,0.00001286437,0.00004945715,0.9924093,0.00003055268,0.00004639017,0.0064934],"study_design_scores_gemma":[0.00003475961,0.0008709351,0.03579706,0.00001458485,0.00006159564,0.001047635,0.0001585003,0.01176794,0.9478816,0.0001925173,0.002130562,0.0000423675],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9312132,0.001896568,0.06291045,0.0002936045,0.0001070798,0.0001511015,0.001664584,0.00053864,0.001224818],"genre_scores_gemma":[0.8399798,0.001499522,0.1524436,0.0002261966,0.00003388747,0.0001320846,0.002722338,0.00006388559,0.002898655],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0008115178,"threshold_uncertainty_score":0.002393305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03303453424199383,"score_gpt":0.3295097356143049,"score_spread":0.2964752013723111,"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."}}