{"id":"W2614737449","doi":"10.1021/acs.analchem.7b01098","title":"Development of High-Performance Chemical Isotope Labeling LC–MS for Profiling the Carbonyl Submetabolome","year":2017,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Alberta Innovates - Health Solutions; Alberta Innovates - Technology Futures; University of Alberta; Alberta Innovates; Ministerul Cercetării, Inovării şi Digitalizării; Genome Canada","keywords":"Chemistry; Chromatography; Electrospray ionization; Reproducibility; Acetaldehyde; Isotope; Chemical ionization; Mass spectrometry; Electrospray; Ionization; Analytical Chemistry (journal); Organic chemistry","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":[0.001053025,0.0006862486,0.0003823899,0.0006396521,0.0003572682,0.0005818729,0.0006188048,0.0006231204,0.000785952],"category_scores_gemma":[0.0006848106,0.0004052213,0.000300672,0.0003789569,0.0004162573,0.0006348411,0.0003983602,0.0008447502,0.0006325072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004505335,"about_ca_system_score_gemma":0.0008114912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007124277,"about_ca_topic_score_gemma":0.001812063,"domain_scores_codex":[0.9994602,0.00008376883,0.00002843502,0.0001614135,0.0002263686,0.00003976688],"domain_scores_gemma":[0.9995653,0.00009581075,0.00006748152,0.00005302033,0.000171814,0.00004652924],"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":[0.00002418356,0.00001638546,0.0002012,0.00002576429,0.000004628587,0.00001192576,0.000006154478,0.00008426583,0.9950765,0.00007331069,0.00003933777,0.004436349],"study_design_scores_gemma":[0.0000121012,0.0001522239,0.001930771,0.000003806746,0.00001341359,0.0001173554,0.000008028429,0.004811885,0.9911864,0.0001016602,0.001647809,0.00001450712],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2441887,0.001575317,0.7477447,0.0003210037,0.0001060115,0.0005444273,0.001077792,0.001772339,0.002669717],"genre_scores_gemma":[0.1846941,0.001039304,0.8096076,0.0002557783,0.00005017082,0.0006093078,0.001331568,0.0001472918,0.00226496],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001053025,"threshold_uncertainty_score":0.005568981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01911056948059912,"score_gpt":0.2671769222708854,"score_spread":0.2480663527902863,"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."}}