{"id":"W2030392928","doi":"10.1016/j.jprot.2014.08.004","title":"Effects of sample injection amount and time-of-flight mass spectrometric detection dynamic range on metabolome analysis by high-performance chemical isotope labeling LC–MS","year":2014,"lang":"en","type":"article","venue":"Journal of Proteomics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Alberta Innovates; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Genome Canada","keywords":"Metabolome; Mass spectrometry; Chromatography; Isotope; Chemistry; Range (aeronautics); Analytical Chemistry (journal); Dynamic range; Sample (material); Metabolomics; Time-of-flight mass spectrometry; Quadrupole time of flight; Tandem mass spectrometry; Materials science; Computer science; Ionization; Physics","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":[0.003823713,0.001478387,0.0006346714,0.000609467,0.0007736322,0.001461613,0.0007705585,0.001275461,0.001640366],"category_scores_gemma":[0.005922669,0.001172831,0.0004845391,0.0006284757,0.00115644,0.001589915,0.00072145,0.0008388179,0.0005730652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004931737,"about_ca_system_score_gemma":0.0007586805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001282625,"about_ca_topic_score_gemma":0.002527184,"domain_scores_codex":[0.9970331,0.0009004166,0.0002880656,0.0008034721,0.0006626983,0.0003121464],"domain_scores_gemma":[0.9952331,0.003696951,0.0002313465,0.0002452501,0.0004273715,0.0001659871],"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":[0.004440909,0.000229993,0.0009824883,0.0001547393,0.00007257729,0.0001091805,0.0001260017,0.0004717491,0.98479,0.0001053139,0.0001458041,0.008371281],"study_design_scores_gemma":[0.00003958598,0.0003851908,0.002630692,0.000009945126,0.0001033881,0.0001061464,0.00003743542,0.002499652,0.9934251,0.00006873776,0.0006682333,0.00002579739],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9328107,0.0100539,0.0499455,0.0008976652,0.0005316599,0.000471431,0.0005832466,0.001111936,0.003594128],"genre_scores_gemma":[0.9234123,0.004907736,0.06543225,0.001241148,0.0001642995,0.0005866599,0.0005768806,0.0005623332,0.003116244],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003823713,"threshold_uncertainty_score":0.02022195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002438760874410187,"score_gpt":0.1995776470057037,"score_spread":0.1971388861312935,"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."}}