{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003467351,0.000254935,0.0006441289,0.0001018937,0.00006594134,0.00002312071,0.0005842053,0.0001974792,0.0001257833],"category_scores_gemma":[0.0004838837,0.000233715,0.0002909489,0.0008790322,0.0002416211,0.000007421405,0.0004531587,0.0001474296,0.000004201383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001420593,"about_ca_system_score_gemma":0.00008146132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006865018,"about_ca_topic_score_gemma":0.0000135028,"domain_scores_codex":[0.9981025,0.0000371869,0.0005329234,0.000724057,0.0002506543,0.0003527027],"domain_scores_gemma":[0.9983324,0.0000564883,0.000193115,0.001115599,0.0002099048,0.00009253983],"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":[0.0001782368,0.0004020377,0.0227684,0.00008857987,0.006690626,0.000007822221,0.00001610491,0.005326573,0.9510363,0.007694934,0.005703271,0.0000871652],"study_design_scores_gemma":[0.0009639431,0.0001251072,0.004851043,0.000008671086,0.006171755,0.000004975388,0.0004545198,0.1674913,0.7908471,0.0007177965,0.02756423,0.000799555],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829253,0.002941687,0.004320322,0.0001221161,0.00001677312,0.0001046167,0.0007936996,0.0000180329,0.008757439],"genre_scores_gemma":[0.9882671,0.0002822406,0.00541769,0.00005594538,0.0001618784,0.000006939061,0.004317502,0.00002267241,0.001468045],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1621648,"threshold_uncertainty_score":0.953062,"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."}}