{"id":"W2178999423","doi":"10.1021/acs.analchem.5b03209","title":"Nanoflow LC–MS for High-Performance Chemical Isotope Labeling Quantitative Metabolomics","year":2015,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Innovates; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Genome Canada","keywords":"Chemistry; Chromatography; Metabolomics; Metabolite; Metabolome; Analyte; Mass spectrometry; Liquid chromatography–mass spectrometry; Urine; Isotope; Biochemistry","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":[0.002173335,0.001616166,0.001080576,0.001271962,0.0008274324,0.0006670604,0.0009558048,0.001172353,0.006718877],"category_scores_gemma":[0.001501072,0.0005944096,0.0007704389,0.0009250614,0.0005671173,0.001186467,0.0007935952,0.002031262,0.004212396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001053982,"about_ca_system_score_gemma":0.0009498888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008254514,"about_ca_topic_score_gemma":0.001818987,"domain_scores_codex":[0.9986246,0.0002800765,0.0001117882,0.0003283369,0.0005454075,0.0001097855],"domain_scores_gemma":[0.9994345,0.000198256,0.00006989871,0.00007464566,0.0001854545,0.00003709206],"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":[0.00008425139,0.00006404632,0.0001504627,0.0003101102,0.00002334671,0.00008506748,0.00003477094,0.0002420527,0.9659767,0.001229294,0.001751821,0.03004798],"study_design_scores_gemma":[0.00006524247,0.000414902,0.001411114,0.00007197137,0.00004601128,0.0006217327,0.00002191048,0.01261036,0.9413913,0.001677229,0.04157722,0.00009105846],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03989747,0.008510493,0.9288789,0.000836688,0.0007373222,0.001140425,0.003685881,0.008076121,0.008236673],"genre_scores_gemma":[0.09371132,0.004484057,0.8868585,0.001252558,0.0002766092,0.00210407,0.004803026,0.0008720046,0.005637865],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006718877,"threshold_uncertainty_score":0.02247685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02817553330730575,"score_gpt":0.2818567809793708,"score_spread":0.253681247672065,"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."}}