{"id":"W2409556733","doi":"10.1021/acs.analchem.5b03126","title":"MyCompoundID MS/MS Search: Metabolite Identification Using a Library of Predicted Fragment-Ion-Spectra of 383,830 Possible Human Metabolites","year":2015,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":106,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Alberta Innovates - Health Solutions; Alberta Innovates - Technology Futures; Alberta Innovates; Ministerul Cercetării, Inovării şi Digitalizării; Genome Canada","keywords":"Metabolite; Metabolome; Chemistry; Metabolomics; In silico; Computational biology; Identification (biology); Mass spectrometry; Chromatography; Biochemistry; Biology","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":[0.0003859342,0.0002507411,0.0005495248,0.00009948154,0.00007277253,0.00004148379,0.0003775978,0.000196417,0.0001438428],"category_scores_gemma":[0.0001880907,0.0002359915,0.0002442867,0.000452432,0.0003077112,0.00002392844,0.0002878403,0.0001690862,0.000002058788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001930365,"about_ca_system_score_gemma":0.0001754124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005830481,"about_ca_topic_score_gemma":0.000001339072,"domain_scores_codex":[0.9979609,0.00006806896,0.0007002389,0.0004860585,0.0004189902,0.0003656887],"domain_scores_gemma":[0.9985811,0.00002365883,0.0002741002,0.0006183936,0.0002656781,0.0002370963],"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":[0.00007655154,0.0002279272,0.007458152,0.0001358252,0.0004490823,0.000002194827,0.00005040693,0.0000347139,0.9893681,0.001469084,0.0006846383,0.00004329245],"study_design_scores_gemma":[0.0006406872,0.00007233279,0.004146403,0.00002062071,0.0002534926,0.000008288785,0.000167859,0.0006624924,0.9886683,0.0007963401,0.004344891,0.0002183064],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876549,0.003723026,0.0007039252,0.00008039725,0.00007581226,0.0001397665,0.0002333679,0.00002094804,0.007367813],"genre_scores_gemma":[0.9952657,0.0005160849,0.001751488,0.00002851116,0.0003250829,0.000007505133,0.0005803118,0.00003427132,0.001491055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007610747,"threshold_uncertainty_score":0.9623452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03558539018579833,"score_gpt":0.2933924032636898,"score_spread":0.2578070130778914,"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."}}