{"id":"W2045074061","doi":"10.1021/ac060209g","title":"Targeted Profiling:  Quantitative Analysis of <sup>1</sup>H NMR Metabolomics Data","year":2006,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":904,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Metabolomics Innovation Centre; Chenomx (Canada); University of Calgary","funders":"","keywords":"Chemistry; Metabolomics; Nuclear magnetic resonance spectroscopy; Analytical Chemistry (journal); Chemometrics; Metabolite profiling; NMR spectra database; Biological system; Proton NMR; Principal component analysis; Metabolite; Two-dimensional nuclear magnetic resonance spectroscopy; Spectral line; Chromatography; Artificial intelligence; Stereochemistry; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007271244,0.000846283,0.0006284769,0.0007492804,0.0001870215,0.0007694472,0.0005401364,0.0004400226,0.001539136],"category_scores_gemma":[0.001333284,0.0003032708,0.0003732273,0.001050031,0.0004070113,0.0008524538,0.0005456299,0.0006033067,0.001444356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003147415,"about_ca_system_score_gemma":0.0003178786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003569933,"about_ca_topic_score_gemma":0.0006346192,"domain_scores_codex":[0.999281,0.0001292747,0.00003927582,0.0002292126,0.0002739677,0.0000472088],"domain_scores_gemma":[0.9992059,0.0003222899,0.0001644142,0.0001035879,0.0001691272,0.00003476517],"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":[0.0001910546,0.00003912967,0.001104323,0.0001889842,0.0000390075,0.00006220536,0.00004338222,0.001537107,0.9244033,0.000518909,0.000502063,0.0713705],"study_design_scores_gemma":[0.00001189796,0.0002621067,0.006965562,0.00002195896,0.00006919516,0.0003595689,0.0000459603,0.04640581,0.9401,0.001394342,0.004319768,0.00004394903],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1061223,0.001429407,0.8854275,0.0003060341,0.00009851577,0.0001690007,0.001511005,0.003213381,0.00172288],"genre_scores_gemma":[0.3487251,0.0031404,0.6423472,0.0005573428,0.0001216315,0.0004603181,0.002038383,0.0005037935,0.002105772],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001539136,"threshold_uncertainty_score":0.005148947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02602408457826481,"score_gpt":0.2940716649469818,"score_spread":0.268047580368717,"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."}}