{"id":"W2315772661","doi":"10.1021/acs.jproteome.6b00070","title":"High-Performance Chemical Isotope Labeling Liquid Chromatography–Mass Spectrometry for Profiling the Metabolomic Reprogramming Elicited by Ammonium Limitation in Yeast","year":2016,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Genome Canada; Canadian Institutes of Health Research; Alberta Innovates - Health Solutions; Alberta Innovates - Technology Futures","keywords":"Metabolome; Metabolomics; Metabolite; Yeast; Metabolic engineering; Chemistry; Lipidomics; Chromatography; Mass spectrometry; Metabolic pathway; Metabolism; Metabolic flux analysis; Liquid chromatography–mass spectrometry; Biochemistry; Biology","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":[0.0004983064,0.0006399408,0.0004273784,0.0007230368,0.0003367604,0.0005956895,0.0002884062,0.0004097334,0.000369839],"category_scores_gemma":[0.0004765236,0.0001927859,0.0004034362,0.0007116873,0.0002205523,0.0003585351,0.0003217864,0.00053597,0.0003212984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004522887,"about_ca_system_score_gemma":0.0006802146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00190742,"about_ca_topic_score_gemma":0.004102204,"domain_scores_codex":[0.9996932,0.00004764411,0.00002490911,0.00007194897,0.0001357917,0.00002657336],"domain_scores_gemma":[0.9997467,0.00006018761,0.00004913996,0.00003028693,0.000078932,0.00003476661],"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":[0.00005323167,0.00001250436,0.0005046835,0.00002849636,0.000009116875,0.00002399719,0.00000760751,0.000142865,0.9964791,0.00004013442,0.00003434388,0.002663917],"study_design_scores_gemma":[0.000009933256,0.00008492633,0.006622843,0.000006930457,0.00003397606,0.0001224108,0.00002891151,0.005108679,0.9862825,0.0001077153,0.001573399,0.00001785336],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7800385,0.004631038,0.2028056,0.000435046,0.000110544,0.0002855342,0.007096873,0.001811598,0.002785209],"genre_scores_gemma":[0.7322924,0.005216947,0.2533655,0.0004718901,0.00004532818,0.0003050243,0.005527239,0.0003226179,0.002453193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00190742,"threshold_uncertainty_score":0.003792584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01986994100848738,"score_gpt":0.293028958010614,"score_spread":0.2731590170021266,"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."}}