{"id":"W1975775976","doi":"10.1021/ac800954c","title":"Analysis of Metabolomic Data Using Support Vector Machines","year":2008,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":369,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Metabolomics; Support vector machine; Chromatography; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001860677,0.0001818321,0.0005140076,0.00007384378,0.00006672781,0.000007096311,0.0004842392,0.0001235026,0.0003655833],"category_scores_gemma":[0.000249866,0.0001634758,0.0002413931,0.0005483021,0.0002091515,0.000004990456,0.0004575244,0.0000926725,0.000002711877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000976753,"about_ca_system_score_gemma":0.00009162582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004773491,"about_ca_topic_score_gemma":0.00000822426,"domain_scores_codex":[0.9986718,0.00001929569,0.0003521604,0.0005152927,0.0001910722,0.0002503646],"domain_scores_gemma":[0.9986345,0.00002041917,0.0001277318,0.001021467,0.00009326328,0.0001026034],"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":[0.00005688558,0.0001129379,0.04685397,0.00002838311,0.003797685,0.00001177213,0.00001577029,0.0001360787,0.9473743,0.00003066429,0.001507379,0.00007417393],"study_design_scores_gemma":[0.000997456,0.000109265,0.06093808,0.00000722711,0.00829855,0.00008978962,0.00009241563,0.0687817,0.8405894,0.00003172226,0.01917697,0.000887397],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957056,0.0007796948,0.001045268,0.00006612633,0.00003595412,0.00003820643,0.0002771391,0.00000813817,0.002043867],"genre_scores_gemma":[0.9962456,0.0004868289,0.001251484,0.00004954936,0.0001592146,0.00000152634,0.0008490513,0.00001524168,0.000941519],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1067849,"threshold_uncertainty_score":0.6666347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05111351934831769,"score_gpt":0.3129713055520993,"score_spread":0.2618577862037816,"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."}}