{"id":"W4392139157","doi":"10.1021/acs.analchem.3c04046","title":"Reducing Quantitative Uncertainty Caused by Data Processing in Untargeted Metabolomics","year":2024,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Saskatchewan Health; Saskatchewan Health Authority; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Canada Foundation for Innovation","keywords":"Metabolomics; Set (abstract data type); Computational model; Variation (astronomy); Chemistry; Data set; Support vector machine; Computer science; Data mining; Artificial intelligence; Biological system; Chromatography","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":[0.02134518,0.001740219,0.001570135,0.002389393,0.001021062,0.003476692,0.00258574,0.001093162,0.001588328],"category_scores_gemma":[0.06867053,0.0008797257,0.001730791,0.002251694,0.001757163,0.003089119,0.003882944,0.002519777,0.0006740658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001356639,"about_ca_system_score_gemma":0.00232785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002267063,"about_ca_topic_score_gemma":0.00348811,"domain_scores_codex":[0.984468,0.005178817,0.001126715,0.003312352,0.00547485,0.0004391413],"domain_scores_gemma":[0.9441019,0.03850543,0.004181754,0.00865616,0.004141937,0.0004127991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002554905,0.0005719071,0.0516886,0.002429985,0.001470314,0.001079993,0.002087448,0.1772712,0.1983838,0.0193547,0.00878624,0.5343208],"study_design_scores_gemma":[0.0001232032,0.0005894332,0.0271031,0.000266263,0.0003301688,0.0009483928,0.0003861758,0.6599977,0.2397859,0.05136227,0.0188008,0.000306566],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08260205,0.0009057743,0.9057792,0.0006806339,0.0001667501,0.0001682274,0.0008425408,0.007560291,0.001294576],"genre_scores_gemma":[0.4150223,0.0003527884,0.5782543,0.0008907008,0.0001063734,0.0003721619,0.002067236,0.002281612,0.0006524303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02134518,"threshold_uncertainty_score":0.1128855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03070700008498107,"score_gpt":0.322156795332805,"score_spread":0.2914497952478239,"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."}}