{"id":"W4283817339","doi":"10.1101/2022.07.01.498527","title":"Serotyping <i>Salmonella</i> Enteritidis and Typhimurium Using Whole Cell Matrix Assisted Laser Desorption Ionization – Time of Flight Mass Spectrometry (MALDI-TOF MS) through Multivariate Analysis and Artificial Intelligence","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bacterial Identification and Susceptibility Testing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ontario Agri-Food Innovation Alliance; University of Guelph","keywords":"Salmonella enteritidis; Salmonella; Serotype; Mass spectrometry; Artificial intelligence; Artificial neural network; Multivariate statistics; Computer science; Analytical Chemistry (journal); Chromatography; Chemistry; Biology; Machine learning; Microbiology; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005697419,0.0003397124,0.0004580763,0.0002619255,0.0002024818,0.0001941808,0.0002211642,0.0003433833,0.00009016787],"category_scores_gemma":[0.0001623385,0.0004137303,0.0001368805,0.0007803699,0.0001123815,0.00002820233,0.0004739824,0.0002619867,0.0000038262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008662577,"about_ca_system_score_gemma":0.0001232597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001235877,"about_ca_topic_score_gemma":0.000006505745,"domain_scores_codex":[0.9976385,0.0002819588,0.000665073,0.0009481065,0.0001971554,0.0002692545],"domain_scores_gemma":[0.9982709,0.0000299545,0.0005983218,0.0006712078,0.000327477,0.0001021923],"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":[0.00006342371,0.0001100428,0.008321202,0.0002474497,0.000207565,0.000003024945,0.00002060243,0.0005420286,0.9904583,0.00001945216,0.000004112961,0.000002792744],"study_design_scores_gemma":[0.000143823,0.00005181122,0.03601369,0.00006749142,0.000463857,6.669652e-8,0.00001969089,0.007859327,0.9548481,0.000007222609,0.0000859772,0.0004390154],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9294525,0.0003965527,0.06917321,0.00003773098,0.0002325152,0.0003652398,0.0002909866,0.00004792402,0.000003318505],"genre_scores_gemma":[0.9764714,0.0001625129,0.02305653,0.00002594244,0.000160053,0.00002386007,0.00002912028,0.00005706971,0.00001354429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04701885,"threshold_uncertainty_score":0.9998314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01790159755057569,"score_gpt":0.2514507575273915,"score_spread":0.2335491599768158,"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."}}