{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00299023,0.0001150206,0.0001923692,0.0002668138,0.00008018481,0.000036641,0.0002729961,0.00012644,0.000002070453],"category_scores_gemma":[0.0007115689,0.00006826483,0.00008986949,0.0005109327,0.00008872407,0.00002013238,0.00004721992,0.0003089466,0.000002107434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004599008,"about_ca_system_score_gemma":0.0000788467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007162389,"about_ca_topic_score_gemma":6.43219e-7,"domain_scores_codex":[0.9985497,0.0001155852,0.0004163214,0.0002442402,0.0002901014,0.0003840412],"domain_scores_gemma":[0.9991102,0.00002322547,0.000165462,0.0002238882,0.0004173001,0.00005990718],"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":[0.0006290101,0.0000484572,0.0001695962,0.00005712542,0.00004534652,6.248214e-7,0.00001701135,0.00002431093,0.9965315,0.00001842728,0.0001829481,0.002275573],"study_design_scores_gemma":[0.0007023936,0.0005758489,0.0001862888,0.0001054806,0.000009659298,0.0000262883,0.00004071668,0.00003521214,0.9955174,0.00002584975,0.002679781,0.00009511324],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900813,0.001557507,0.006843346,0.000806678,0.0001244107,0.0005688868,0.000008144822,0.000006579297,0.000003125037],"genre_scores_gemma":[0.9746405,0.000905029,0.02350059,0.000008858869,0.0007560504,0.00007352095,0.0000101265,0.00002323932,0.00008209772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01665725,"threshold_uncertainty_score":0.2783759,"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."}}