{"id":"W3118888888","doi":"10.1021/acs.analchem.0c04113","title":"Patterned Signal Ratio Biases in Mass Spectrometry-Based Quantitative Metabolomics","year":2021,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Canada Foundation for Innovation","keywords":"Metabolomics; Chemistry; SIGNAL (programming language); Mass spectrometry; Linear regression; Biological system; Polynomial; Electrospray ionization; Nonlinear system; Compression ratio; Analytical Chemistry (journal); Statistics; Chromatography; Computer science; Mathematics","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":[0.006709855,0.001438281,0.0007964741,0.001022234,0.0003440987,0.001055059,0.001402416,0.001042439,0.0006429805],"category_scores_gemma":[0.01418254,0.0006617467,0.0009508464,0.0009684044,0.001200657,0.001242682,0.001457354,0.001236577,0.0005089328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007478544,"about_ca_system_score_gemma":0.00075148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001022399,"about_ca_topic_score_gemma":0.001378621,"domain_scores_codex":[0.9942496,0.001631834,0.0002842229,0.001706337,0.001887422,0.0002406323],"domain_scores_gemma":[0.9954972,0.002493142,0.0007518459,0.0005899259,0.0006144735,0.00005345838],"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":[0.0004601061,0.0001428524,0.006420997,0.0005857925,0.0002475349,0.0003827793,0.0002808099,0.03750791,0.8411759,0.003470163,0.0005826299,0.1087425],"study_design_scores_gemma":[0.00001716962,0.0002843683,0.004720853,0.00004251645,0.0001108307,0.0002933165,0.00004350014,0.1950669,0.7932962,0.003228026,0.002797428,0.00009879416],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.152732,0.002446916,0.8394995,0.000418955,0.0001744741,0.0001614053,0.0003040656,0.002667573,0.001595131],"genre_scores_gemma":[0.6840417,0.001475867,0.3112013,0.0006354907,0.00009807052,0.000270768,0.0004927255,0.0005475144,0.001236557],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006709855,"threshold_uncertainty_score":0.03548557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02619647074090451,"score_gpt":0.2890937879227736,"score_spread":0.2628973171818691,"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."}}