{"id":"W2325415461","doi":"10.1021/ac403171u","title":"Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry: A High-Throughput Platform for Metabolomics with High Data Fidelity","year":2013,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":139,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Genomics Institute; Ontario Genomics","keywords":"Chemistry; Metabolomics; Mass spectrometry; Capillary electrophoresis; Throughput; Chromatography; Reproducibility; Sample (material); Resolution (logic); Analytical Chemistry (journal); Capillary electrophoresis–mass spectrometry; Electrospray ionization; Computer science; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002741767,0.0004435527,0.0005940538,0.00005440513,0.0002044917,0.0001044622,0.0006781549,0.0002807768,0.0002869306],"category_scores_gemma":[0.0003482954,0.0003637615,0.0001533533,0.0003296783,0.000199009,0.00002740915,0.0004039881,0.0002613322,0.00001915194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008280126,"about_ca_system_score_gemma":0.0001511757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002809637,"about_ca_topic_score_gemma":0.00003116563,"domain_scores_codex":[0.9972879,0.00002033978,0.0004837984,0.001148916,0.0003183507,0.0007406981],"domain_scores_gemma":[0.9977917,0.00007408289,0.0001863713,0.001429667,0.0002731934,0.0002449709],"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":[0.000342952,0.0002336661,0.0009402943,0.0001149816,0.001324369,0.000005238984,0.000005874093,0.00005320089,0.9708871,0.0008992628,0.02473021,0.0004628428],"study_design_scores_gemma":[0.002904787,0.000869444,0.003744622,0.00001931197,0.0006751089,0.00005301497,0.0001774515,0.002995107,0.9526941,0.003076079,0.03163835,0.001152624],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9700456,0.001774149,0.02514755,0.0007304382,0.0001261822,0.0006523909,0.0003678669,0.00004937059,0.001106484],"genre_scores_gemma":[0.9589034,0.00127859,0.03480324,0.0003393931,0.0007644933,0.0001511329,0.002039996,0.00006680302,0.001652893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.018193,"threshold_uncertainty_score":0.9998814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01692031468125049,"score_gpt":0.2535829437288579,"score_spread":0.2366626290476074,"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."}}