{"id":"W1983624870","doi":"10.1093/bib/bbm030","title":"Current Progress in computational metabolomics","year":2007,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":227,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Institute for Nanotechnology","funders":"","keywords":"Metabolomics; Cheminformatics; Computer science; Field (mathematics); Data science; Computational model; Computational biology; Bioinformatics; Artificial intelligence; Biology; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006793739,0.0004540824,0.001211609,0.00055983,0.00004540935,0.00005296295,0.0003754106,0.0003654908,0.000005246112],"category_scores_gemma":[0.0001106683,0.0004078226,0.0002772536,0.0005879994,0.0001378252,0.000008355796,0.0003344474,0.0004412107,0.00001816899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000766849,"about_ca_system_score_gemma":0.0002626594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006169794,"about_ca_topic_score_gemma":0.00002663168,"domain_scores_codex":[0.9975834,0.00004655969,0.001305527,0.0003282454,0.0002407677,0.000495459],"domain_scores_gemma":[0.9989853,0.00004922887,0.000517039,0.0003117298,0.00006731122,0.00006937143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000796019,0.00009549219,0.0001498191,0.004404494,0.00007439421,0.000003734696,0.0000528515,0.00001492329,1.361072e-7,0.001364053,0.00105305,0.9927791],"study_design_scores_gemma":[0.0003701283,0.00003824049,0.0001169333,0.001314744,0.00007048176,0.00002660928,0.00001667717,0.0001839236,0.000002786356,0.0001304364,0.9972976,0.0004313941],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00008707245,0.9974833,0.0009939289,0.00002731475,0.0002935212,0.0005988058,0.00007311044,0.00001125603,0.0004317424],"genre_scores_gemma":[0.00001630956,0.9826627,0.0162106,0.0001296772,0.0001267702,0.00006899517,0.0007218165,0.00003840731,0.00002476137],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9962446,"threshold_uncertainty_score":0.9998373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0384868866720468,"score_gpt":0.3516625370312874,"score_spread":0.3131756503592406,"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."}}