{"id":"W4379093872","doi":"10.1002/mrc.5372","title":"Automated identification and quantification of metabolites in human fecal extracts by nuclear magnetic resonance spectroscopy","year":2023,"lang":"en","type":"review","venue":"Magnetic Resonance in Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genome Alberta; Multiple Sclerosis International Federation; Canada Foundation for Innovation","keywords":"Metabolite; Chemistry; Profiling (computer programming); Metabolite profiling; Metabolomics; Feces; Chromatography; Nuclear magnetic resonance spectroscopy; Analytical Chemistry (journal); Computer science; Stereochemistry; Biochemistry; Biology; Microbiology","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":[0.001650169,0.001298576,0.0009201177,0.00262277,0.0004093334,0.001044243,0.0006391345,0.0007237307,0.003485693],"category_scores_gemma":[0.003375796,0.0005496984,0.0007675476,0.001088483,0.0003035378,0.0007799222,0.0009000208,0.0004119408,0.00200204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004670653,"about_ca_system_score_gemma":0.0007996773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001393089,"about_ca_topic_score_gemma":0.002502154,"domain_scores_codex":[0.9992529,0.0001261137,0.00004478316,0.0002781338,0.0002546615,0.00004342827],"domain_scores_gemma":[0.9984842,0.0006431557,0.0002721029,0.0001767943,0.0003642862,0.00005959241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001513654,0.0001780768,0.01854604,0.0009153675,0.0002792583,0.0003133512,0.0001457943,0.007477225,0.6804255,0.0008778575,0.009494975,0.2798329],"study_design_scores_gemma":[0.0001586556,0.0005690565,0.03217058,0.00009879986,0.0001761738,0.001081617,0.0001038518,0.1605099,0.7724496,0.00212508,0.03033014,0.0002266498],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.2253968,0.001758553,0.7002259,0.0004121028,0.0001189744,0.0004178622,0.01890054,0.04879009,0.003979254],"genre_scores_gemma":[0.1926482,0.0006548007,0.7930178,0.0002676062,0.00004989589,0.0003733932,0.008936364,0.001732671,0.002319346],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.003485693,"threshold_uncertainty_score":0.01166075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0231916679131545,"score_gpt":0.3170587664057309,"score_spread":0.2938670984925764,"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."}}