{"id":"W2004753393","doi":"10.1002/mrc.2535","title":"Analysis of time course <sup>1</sup>H NMR metabolomics data by multivariate curve resolution","year":2009,"lang":"en","type":"article","venue":"Magnetic Resonance in Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Marine Biosciences","funders":"","keywords":"Chemistry; Multivariate statistics; Metabolomics; Multivariate analysis; Resolution (logic); Nuclear magnetic resonance spectroscopy; Analytical Chemistry (journal); Chromatography; Stereochemistry; Statistics; Artificial intelligence","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":[0.0009150199,0.0008599132,0.0005561848,0.0008477439,0.0001899708,0.0006481978,0.0008114073,0.0006054423,0.003542531],"category_scores_gemma":[0.002704509,0.000250552,0.0006032317,0.002062973,0.0003794061,0.0009127481,0.0004289277,0.00101204,0.001795036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003271907,"about_ca_system_score_gemma":0.0004172925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006673453,"about_ca_topic_score_gemma":0.001031972,"domain_scores_codex":[0.9995992,0.0000604666,0.000025356,0.0001042675,0.0001853013,0.00002550063],"domain_scores_gemma":[0.9984124,0.0007743388,0.0002419643,0.0001913301,0.0003386177,0.00004141349],"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":[0.0004928686,0.000119071,0.004278873,0.0007701494,0.0001273519,0.0002470879,0.0001267643,0.0107701,0.7603877,0.002982937,0.003661803,0.2160353],"study_design_scores_gemma":[0.00003920556,0.0006180982,0.03046375,0.00006331202,0.0001605602,0.0009375321,0.0002716931,0.2996489,0.6357511,0.008322126,0.02354188,0.0001817176],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06194455,0.001008172,0.9279056,0.0003379939,0.0001362231,0.0001315402,0.003248551,0.002925877,0.002361551],"genre_scores_gemma":[0.181161,0.003265008,0.8033797,0.0003816771,0.0001686596,0.000449995,0.007620439,0.0008755398,0.002698011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003542531,"threshold_uncertainty_score":0.01185101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01163570862995624,"score_gpt":0.2649014317630716,"score_spread":0.2532657231331154,"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."}}