{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003092663,0.00038116,0.0002275357,0.0006823393,0.0000931637,0.000315751,0.0001479413,0.0001485482,0.0005501811],"category_scores_gemma":[0.0005113618,0.00009246248,0.0003186013,0.0004629801,0.0001233259,0.0002264778,0.0002239134,0.0002161037,0.0002153531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001300308,"about_ca_system_score_gemma":0.0002006557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001294333,"about_ca_topic_score_gemma":0.001793226,"domain_scores_codex":[0.9998234,0.00003302823,0.00002254403,0.00005275752,0.00004892161,0.00001942491],"domain_scores_gemma":[0.999827,0.00004547136,0.00004639651,0.0000160883,0.00005239229,0.00001264644],"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":[0.0003918866,0.00008805628,0.02780554,0.0001363386,0.00004476789,0.0001066012,0.00006817088,0.002010024,0.917534,0.0001153426,0.0002265674,0.05147278],"study_design_scores_gemma":[0.00001758643,0.0008132444,0.2233954,0.00003503003,0.000120561,0.0007404357,0.0002509126,0.09406611,0.6780041,0.0004082964,0.002100783,0.00004749164],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9489875,0.0004231543,0.04775083,0.00006942376,0.00001608895,0.00007191224,0.001302997,0.0004820353,0.0008960799],"genre_scores_gemma":[0.9414638,0.0002688441,0.05625775,0.00002811467,0.00000537181,0.00003085785,0.001398825,0.00003610539,0.000510284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001294333,"threshold_uncertainty_score":0.00257355,"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."}}