{"id":"W2473466345","doi":"10.1021/acs.analchem.5b01434","title":"Quantitative Metabolome Analysis Based on Chromatographic Peak Reconstruction in Chemical Isotope Labeling Liquid Chromatography Mass Spectrometry","year":2015,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":70,"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":"Chemistry; Metabolomics; Chromatography; Mass spectrometry; Isotope; Metabolome; Metabolite; Analytical Chemistry (journal); Liquid chromatography–mass spectrometry; Quantitative analysis (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":[0.003084528,0.001301046,0.00100396,0.003508045,0.0006133988,0.001590638,0.001333022,0.0006326018,0.003044541],"category_scores_gemma":[0.003961706,0.0009350757,0.001093612,0.00266325,0.0007904735,0.001370325,0.001122897,0.001769691,0.002092582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006484768,"about_ca_system_score_gemma":0.000884809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008252308,"about_ca_topic_score_gemma":0.001027694,"domain_scores_codex":[0.9979261,0.0004973882,0.0001390205,0.0005260659,0.0008100281,0.0001013703],"domain_scores_gemma":[0.9984131,0.0006616329,0.0002384725,0.0002563304,0.000382119,0.00004819958],"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":[0.0005071293,0.0002420506,0.002687035,0.0003560568,0.0001196546,0.000107462,0.0001038775,0.004328376,0.8468891,0.004008545,0.001843713,0.1388071],"study_design_scores_gemma":[0.00005104249,0.0002346482,0.005739782,0.00003391697,0.00008290262,0.0005127284,0.00004703537,0.1808293,0.7988302,0.004091398,0.00939305,0.0001538835],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01258877,0.0002410112,0.9797444,0.00005110196,0.0000336484,0.0001943831,0.0009404123,0.005444293,0.0007619464],"genre_scores_gemma":[0.02411882,0.000214951,0.9732488,0.00005155612,0.00001493771,0.0004144773,0.001013211,0.0004973391,0.0004258797],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003508045,"threshold_uncertainty_score":0.01631272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01758511593305036,"score_gpt":0.2682353819397694,"score_spread":0.250650266006719,"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."}}