{"id":"W4377693808","doi":"10.1021/acs.analchem.2c05371","title":"Dual UHPLC-HRMS Metabolomics and Lipidomics and Automated Data Processing Workflow for Comprehensive High-Throughput Gut Phenotyping","year":2023,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"FP7 Health; Directorate for Biological Sciences; Koning Boudewijnstichting; Bijzonder Onderzoeksfonds UGent; Vlaams Instituut voor Biotechnologie; Vlaamse regering; Queen's University; Universiteit Gent; KU Leuven; Fonds Wetenschappelijk Onderzoek; Rijksuniversiteit Groningen","keywords":"Chemistry; Metabolomics; Lipidomics; Workflow; Throughput; Computational biology; Metabolome; Biochemical engineering; Chromatography; Biochemistry; Computer science; Database","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003931958,0.001573426,0.001129772,0.002045379,0.0007876602,0.001345899,0.001135984,0.001225875,0.004585594],"category_scores_gemma":[0.002540379,0.0008457147,0.001108561,0.001135087,0.0006303191,0.0009305244,0.001784628,0.001347652,0.003809411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006515603,"about_ca_system_score_gemma":0.002293747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001330943,"about_ca_topic_score_gemma":0.002045884,"domain_scores_codex":[0.9974367,0.0003854286,0.0002880349,0.0008099684,0.0008996014,0.0001803491],"domain_scores_gemma":[0.9986299,0.0003618507,0.00019558,0.0002161127,0.0004806955,0.0001158897],"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":[0.001040834,0.000280682,0.004926331,0.0005073803,0.0001633361,0.0003275705,0.0001642593,0.002029238,0.9216415,0.0009156333,0.003325477,0.06467783],"study_design_scores_gemma":[0.0001850093,0.0009183045,0.01822011,0.0001064624,0.0001743463,0.001148307,0.0001285069,0.05839184,0.8932577,0.001933632,0.02523834,0.0002975689],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09783284,0.0009771571,0.8659459,0.0004048001,0.0001903886,0.001507651,0.008641082,0.02249971,0.002000519],"genre_scores_gemma":[0.08290968,0.0005038701,0.9055921,0.0006033381,0.000062463,0.001855349,0.005530493,0.0008514777,0.002091146],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004585594,"threshold_uncertainty_score":0.02079439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04805462392805193,"score_gpt":0.3211813211530538,"score_spread":0.2731266972250019,"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."}}