{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001855342,0.0001238476,0.0003619977,0.0002681679,0.00005445303,0.00003790454,0.0001075633,0.0001581491,0.00003224164],"category_scores_gemma":[0.0003792422,0.00009773058,0.0001060798,0.0007373856,0.0001374464,0.00001603695,0.00005577311,0.00009163714,0.000001057608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001680182,"about_ca_system_score_gemma":0.00002675742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008813784,"about_ca_topic_score_gemma":0.000002085713,"domain_scores_codex":[0.9982187,0.0005423366,0.0007857592,0.0002508048,0.00007986671,0.0001225551],"domain_scores_gemma":[0.9984375,0.0001181242,0.0008596468,0.000156809,0.0003739059,0.0000540513],"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":[0.0002601427,0.00008403251,0.002425359,0.00004394801,0.0001483723,0.000001235662,0.00004748385,0.0004513567,0.9952459,0.000003868663,0.000002518429,0.001285763],"study_design_scores_gemma":[0.0001849162,0.0002680275,0.05126509,0.00002551433,0.0002732238,0.00002434623,0.0001272004,0.008486018,0.9391453,0.0000853834,0.00001032424,0.0001046031],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7559087,0.0001233169,0.2437671,0.00003079624,0.00005014003,0.00008697685,0.00002629719,0.000004943078,0.000001759278],"genre_scores_gemma":[0.8790865,0.0001541635,0.1205767,0.00000568958,0.0000400732,8.497389e-7,0.00008940245,0.000008304426,0.00003830761],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1231903,"threshold_uncertainty_score":0.3985337,"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."}}