{"id":"W2316589045","doi":"10.1021/ac503619q","title":"Development of High-Performance Chemical Isotope Labeling LC–MS for Profiling the Human Fecal Metabolome","year":2014,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Ministry of Science and Technology of the People's Republic of China; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canadian Institutes of Health Research; Alberta Innovates - Health Solutions","keywords":"Chemistry; Metabolome; Profiling (computer programming); Chromatography; Feces; Isotope; Metabolomics; Microbiology","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":[0.0009661779,0.0009677588,0.000528535,0.001008965,0.0004300437,0.0005126296,0.0005379738,0.00107932,0.0007609849],"category_scores_gemma":[0.001085473,0.0003728825,0.0004157372,0.0005975474,0.0003972839,0.0006772962,0.0004972955,0.0008912545,0.0007658091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003675356,"about_ca_system_score_gemma":0.001010556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008623148,"about_ca_topic_score_gemma":0.002313598,"domain_scores_codex":[0.9992732,0.0001547273,0.00004193723,0.0002469157,0.0002345128,0.00004868693],"domain_scores_gemma":[0.9996013,0.00009241308,0.00005953513,0.0000341994,0.0001737728,0.00003884001],"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":[0.00008808901,0.00005116047,0.0007919816,0.0001237384,0.00002700535,0.00005449625,0.00001994231,0.0002564863,0.979736,0.0002248585,0.0001782729,0.01844808],"study_design_scores_gemma":[0.00003232106,0.0004287988,0.003920034,0.0000278845,0.00006424379,0.0004859312,0.00003637234,0.0103965,0.9768339,0.0003292212,0.007390632,0.00005407269],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1752159,0.006415974,0.8072976,0.0006049121,0.000241585,0.001070659,0.002617511,0.002443368,0.004092544],"genre_scores_gemma":[0.179087,0.003935397,0.810335,0.0007309712,0.00009062156,0.001080409,0.002047259,0.0001555133,0.002537753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00107932,"threshold_uncertainty_score":0.005109668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01521149117244627,"score_gpt":0.2748288052135001,"score_spread":0.2596173140410539,"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."}}