{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001599568,0.0008902849,0.0006779171,0.000769547,0.0004104148,0.001246378,0.00114437,0.001054391,0.001339909],"category_scores_gemma":[0.001348118,0.0005859522,0.0003330298,0.0007495965,0.0008221627,0.001076339,0.001164514,0.001241628,0.001008803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007069063,"about_ca_system_score_gemma":0.001159301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008198961,"about_ca_topic_score_gemma":0.001554542,"domain_scores_codex":[0.9985346,0.0001978618,0.00005840396,0.0004444368,0.0006906383,0.00007400054],"domain_scores_gemma":[0.9992975,0.0002112138,0.0001446495,0.0001251941,0.0001500997,0.00007138088],"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":[0.00009742555,0.00002724456,0.0002366537,0.00008789905,0.00001289629,0.00003719244,0.00002684139,0.0002889676,0.9796213,0.001002784,0.000448722,0.01811211],"study_design_scores_gemma":[0.00002085048,0.0001214369,0.0009499803,0.00001326387,0.00001728182,0.0003448439,0.0000107473,0.01210601,0.9770233,0.000632929,0.008724646,0.00003475613],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08635145,0.004793052,0.897943,0.0005892019,0.0002019813,0.0003737502,0.001146155,0.006253908,0.002347559],"genre_scores_gemma":[0.1972258,0.002526015,0.794922,0.0004461424,0.0001198071,0.0004498143,0.001182345,0.0003567288,0.002771396],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001599568,"threshold_uncertainty_score":0.008459389,"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."}}