{"id":"W3216809113","doi":"10.1021/acs.analchem.1c02826","title":"Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring","year":2021,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"NIH Office of the Director; National Cancer Institute; U.S. Department of Health and Human Services; National Institutes of Health; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; European Commission; National Human Genome Research Institute","keywords":"Lipidomics; Metabolomics; Chemistry; Biomarker discovery; NIST; Ion-mobility spectrometry; Biomarker; Computational biology; Mass spectrometry; Bioinformatics; Chromatography; Proteomics; Computer science; Biochemistry; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.02059647,0.001729617,0.0009434947,0.002545286,0.001366975,0.001602113,0.001611521,0.00142667,0.001329516],"category_scores_gemma":[0.01840088,0.0006779798,0.001042825,0.001658566,0.001926988,0.0009033662,0.002396652,0.001367827,0.001148359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008425171,"about_ca_system_score_gemma":0.002457078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001038193,"about_ca_topic_score_gemma":0.001445381,"domain_scores_codex":[0.9776399,0.009517465,0.002319104,0.003894208,0.006127905,0.0005014172],"domain_scores_gemma":[0.9894608,0.002083603,0.001743811,0.003385575,0.003120875,0.0002053362],"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":[0.001331608,0.0007511913,0.01144152,0.0003693792,0.0003966525,0.00008397661,0.0004546977,0.00207962,0.9362187,0.001368601,0.0007641255,0.04473986],"study_design_scores_gemma":[0.000101069,0.00267286,0.01896583,0.00006679872,0.0003090695,0.0003195477,0.0001427791,0.005565186,0.9632218,0.001102881,0.007426063,0.0001061273],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2962201,0.002092712,0.6871784,0.0004193748,0.000467591,0.004793745,0.002354677,0.003338694,0.003134806],"genre_scores_gemma":[0.4234846,0.001847456,0.5579309,0.000608167,0.0002210347,0.0067913,0.005820824,0.0006185363,0.002677214],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02059647,"threshold_uncertainty_score":0.1089259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02254471768150475,"score_gpt":0.3259549999557248,"score_spread":0.3034102822742201,"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."}}