{"id":"W2955786479","doi":"10.1002/lipd.12172","title":"Development of a Rapid Ultra High‐Performance Liquid Chromatography/Tandem Mass Spectrometry Method for the Analysis of <i>sn</i>‐1 and <i>sn</i>‐2 Lysophosphatidic Acid Regioisomers in Mouse Plasma","year":2019,"lang":"en","type":"article","venue":"Lipids","topic":"Sphingolipid Metabolism and Signaling","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation; Diabetes Canada","keywords":"Structural isomer; Chemistry; Chromatography; Lysophosphatidic acid; Tandem mass spectrometry; Mass spectrometry; High-performance liquid chromatography; Metabolism; Glycerol; Extraction (chemistry); Tandem; Biochemistry; Stereochemistry","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":[0.0007894937,0.000215023,0.0005577473,0.0003341495,0.00006153181,0.00001235362,0.0002823174,0.000154042,0.00001760131],"category_scores_gemma":[0.0000456611,0.0001693109,0.0002006422,0.0008894857,0.00008267307,0.00001155426,0.00005472393,0.00009167353,5.504354e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001125847,"about_ca_system_score_gemma":0.00007712201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000182908,"about_ca_topic_score_gemma":0.00001564116,"domain_scores_codex":[0.9984989,0.00005397751,0.000545526,0.0003914287,0.0002069072,0.0003033217],"domain_scores_gemma":[0.999069,0.00009645963,0.0002778263,0.0004237485,0.00007219111,0.00006079143],"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":[0.0003528848,0.00004523324,0.0136426,0.0001236782,0.0008314281,1.233439e-7,0.0003399203,0.0003201039,0.9825828,0.0001041252,0.00001816811,0.001638956],"study_design_scores_gemma":[0.001258611,0.0002402567,0.008949235,0.00002725007,0.0002739099,0.000001419946,0.000294843,0.0008494357,0.9859824,0.000005378012,0.001918436,0.0001987835],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885308,0.00246454,0.008341207,0.00002492043,0.0001692017,0.0003425772,0.00003040727,0.000008137853,0.00008820486],"genre_scores_gemma":[0.9608299,0.001456128,0.03736007,0.00008552855,0.00007961209,0.00004734543,0.0000632572,0.00002349347,0.00005472254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02901886,"threshold_uncertainty_score":0.6904299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007253543337943426,"score_gpt":0.2302358084075136,"score_spread":0.2229822650695701,"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."}}