{"id":"W4388422281","doi":"10.1021/jasms.3c00273","title":"An In Silico Database for Automated Feature Identification of High-Resolution Tandem Mass Spectrometry <sup>13</sup>C-Trimethylation Enhancement Using Diazomethane (<sup>13</sup>C-TrEnDi)-Modified Lipid Data","year":2023,"lang":"en","type":"article","venue":"Journal of the American Society for Mass Spectrometry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario; Canada Foundation for Innovation; Ontario Research Foundation; Carleton University","keywords":"Chemistry; Mass spectrometry; Diazomethane; Tandem mass spectrometry; Derivatization; Database; Fragmentation (computing); Chromatography; Organic chemistry","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":[0.002693572,0.002698907,0.001627703,0.003994964,0.0008775251,0.002558965,0.003355661,0.001973153,0.009888562],"category_scores_gemma":[0.006114647,0.00118092,0.001880632,0.003053854,0.0004649143,0.002237215,0.002078978,0.00147398,0.01146932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001090302,"about_ca_system_score_gemma":0.0020752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001669772,"about_ca_topic_score_gemma":0.002039803,"domain_scores_codex":[0.9985544,0.0002092326,0.0002841606,0.0004920682,0.0003612165,0.00009895219],"domain_scores_gemma":[0.9982944,0.0004979604,0.0003331337,0.0003527385,0.0003626271,0.0001591997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.009586539,0.00152835,0.0316713,0.01350872,0.001966657,0.006708106,0.001251214,0.03582968,0.3048363,0.02020542,0.3003009,0.2726068],"study_design_scores_gemma":[0.001348594,0.001317244,0.02759415,0.0009849829,0.001227947,0.004008583,0.0009608995,0.2593969,0.1909019,0.01722053,0.4945287,0.0005096212],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.04434761,0.002812759,0.2952869,0.0005325104,0.0003498834,0.001307812,0.440781,0.2074904,0.007091099],"genre_scores_gemma":[0.05252923,0.001269612,0.2116682,0.0002830409,0.00004004388,0.001247291,0.7253045,0.005449217,0.002208853],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.009888562,"threshold_uncertainty_score":0.03308052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02692934656908508,"score_gpt":0.3163961139637949,"score_spread":0.2894667673947098,"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."}}