{"id":"W2957251338","doi":"10.1038/s41467-019-10900-y","title":"Use cases, best practice and reporting standards for metabolomics in regulatory toxicology","year":2019,"lang":"en","type":"review","venue":"Nature Communications","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":199,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; National Cancer Institute; University of Johannesburg; Johns Hopkins University; Biotechnology and Biological Sciences Research Council; Corteva Agriscience; University of Warwick; Université de Lausanne; European Bioinformatics Institute; Université de Genève; Medical Research Council; Francis Crick Institute; World Health Organization","keywords":"Metabolomics; Good laboratory practice; Regulatory science; Data science; Computer science; Best practice; Risk analysis (engineering); Engineering ethics; Medicine; Bioinformatics; Biology; Political science; Engineering; Quality assurance","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00167285,0.0003030786,0.001220616,0.0001759762,0.0001700812,0.00005296438,0.0005533072,0.001006743,0.000001385842],"category_scores_gemma":[0.01876451,0.0002746603,0.0002566246,0.0002159359,0.0001398451,0.00001187653,0.0008154836,0.0009098179,8.28371e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008906847,"about_ca_system_score_gemma":0.0006105393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001706716,"about_ca_topic_score_gemma":0.0005408141,"domain_scores_codex":[0.9977961,0.0003314504,0.001009973,0.0004896476,0.0001214077,0.0002514448],"domain_scores_gemma":[0.9947401,0.001140528,0.001627732,0.002034619,0.0004056332,0.00005139861],"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":[0.0002169748,0.001173582,0.0005381582,0.0103228,0.003641323,0.00003034968,0.0001233231,0.000003430937,0.001466392,0.08415528,0.03059383,0.8677346],"study_design_scores_gemma":[0.0001762508,0.0001179314,0.0000241202,0.0003392548,0.0007897834,0.0001592895,0.00004820167,0.000002459961,0.00001897655,0.00001787687,0.9980433,0.0002625388],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002606659,0.9970415,0.00002527399,0.0002746314,0.0002628533,0.001105672,0.0006278013,0.000007849324,0.0003937258],"genre_scores_gemma":[0.0007788843,0.982712,0.01461866,0.0001843791,0.0001005559,0.0002634206,0.0009271831,0.00005114238,0.0003637542],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9674495,"threshold_uncertainty_score":0.9999706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1117362138630905,"score_gpt":0.436041648186808,"score_spread":0.3243054343237175,"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."}}