{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001913329,0.0001427895,0.0002868199,0.00001907599,0.00007328137,0.00003153283,0.00006949995,0.0001976743,0.00003562423],"category_scores_gemma":[0.001070626,0.0001480229,0.0001065871,0.0001720264,0.0001727408,0.000007075245,0.0001719665,0.0001467089,2.590036e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003482675,"about_ca_system_score_gemma":0.0001051799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005366281,"about_ca_topic_score_gemma":0.000001128335,"domain_scores_codex":[0.9988452,0.00003339161,0.0003503066,0.0004201595,0.000164629,0.0001863164],"domain_scores_gemma":[0.999149,0.00005129982,0.0001171939,0.0003121301,0.0002755939,0.00009477149],"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":[0.00009398704,0.0001132151,0.08242877,0.00008066865,0.0001242482,0.00000163059,0.000004484883,0.00003878315,0.9169969,0.00001954219,0.00001056225,0.00008724308],"study_design_scores_gemma":[0.0005857075,0.00003483695,0.02616605,0.00001382562,0.00008560919,0.000006726335,0.00005412733,0.001026834,0.9712707,0.00004410454,0.0005679029,0.0001435975],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939001,0.0008267179,0.004740009,0.00001810419,0.0001346515,0.000045749,0.00006199163,0.000007775285,0.0002648746],"genre_scores_gemma":[0.9971153,0.0006153581,0.001579707,0.000006827649,0.0004777935,0.000001491491,0.00005306678,0.00001427704,0.000136213],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05626272,"threshold_uncertainty_score":0.60362,"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."}}