{"id":"W2077545398","doi":"10.1007/s11306-012-0462-0","title":"MetaboLights: towards a new COSMOS of metabolomics data management","year":2012,"lang":"en","type":"article","venue":"Metabolomics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biotechnology and Biological Sciences Research Council; European Commission; European Bioinformatics Institute","keywords":"Metabolomics; Metadata; Workflow; Standardization; Data science; Metabolome; Omics; Data management; Big data; Genomics; Biology; Computer science; Bioinformatics; World Wide Web; Database; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001086268,0.0004559134,0.0008320082,0.0001922767,0.000110545,0.00003296202,0.001330473,0.000207412,0.00009744212],"category_scores_gemma":[0.0001848712,0.0004006789,0.0002383617,0.000391839,0.0001512947,0.00003962902,0.001706002,0.0001708467,0.00003999828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000168617,"about_ca_system_score_gemma":0.0001138081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006190615,"about_ca_topic_score_gemma":0.00001519907,"domain_scores_codex":[0.99727,0.0001225856,0.0007124876,0.0007161713,0.0003744662,0.000804309],"domain_scores_gemma":[0.9970403,0.00001939219,0.0003627716,0.002143992,0.0001159678,0.0003175515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009052386,0.001667573,0.01625301,0.0002836022,0.008728852,0.000007871138,0.0004968735,0.00006540299,0.570682,0.1911138,0.1063164,0.1034793],"study_design_scores_gemma":[0.001215494,0.00006810002,0.009789709,0.00000619602,0.000818397,0.00001565881,0.0001987189,0.00003875502,0.1589348,0.0005630601,0.8278701,0.0004810379],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8079524,0.1329738,0.03487959,0.0006096356,0.003528443,0.001207678,0.0009056846,0.00008511016,0.01785767],"genre_scores_gemma":[0.8076949,0.03507449,0.1482318,0.0007306829,0.00191635,0.00004657019,0.001011073,0.0001367693,0.005157434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7215536,"threshold_uncertainty_score":0.9998445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03755016967609196,"score_gpt":0.2886203809938966,"score_spread":0.2510702113178046,"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."}}