{"id":"W2002242662","doi":"10.1007/s11306-007-0081-3","title":"Proposed minimum reporting standards for data analysis in metabolomics","year":2007,"lang":"en","type":"article","venue":"Metabolomics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":433,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Institute for Biodiagnostics; Chenomx (Canada)","funders":"Biotechnology and Biological Sciences Research Council; National Institutes of Health; Medical Research Council; Vetenskapsrådet","keywords":"Computer science; Univariate; Data mining; Outlier; Data set; Statistical hypothesis testing; Multivariate statistics; Machine learning; Artificial intelligence; Statistics; 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":[0.008432411,0.0003450283,0.0009549682,0.0004588936,0.0001460478,0.00005736191,0.0007256825,0.0002431169,0.00001662078],"category_scores_gemma":[0.003756989,0.0003225229,0.0003299521,0.0009315357,0.0001121175,0.00001582926,0.0005596285,0.0001791338,0.000001446539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005258734,"about_ca_system_score_gemma":0.0002530231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005171947,"about_ca_topic_score_gemma":0.001050108,"domain_scores_codex":[0.9961176,0.00006762615,0.00161838,0.00110151,0.0003396855,0.0007552075],"domain_scores_gemma":[0.9968536,0.00007954452,0.001036331,0.001562544,0.0003386539,0.0001292665],"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":[0.001745437,0.0005044242,0.1209935,0.0001131071,0.00536453,0.00003430565,0.0002062807,0.0003572498,0.8416544,0.006310321,0.004524352,0.01819209],"study_design_scores_gemma":[0.003755219,0.000324112,0.04587111,0.00001061275,0.002832344,0.00001941088,0.0007079301,0.00515807,0.2909637,0.0011184,0.6479465,0.001292565],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8249098,0.005484548,0.1667935,0.0001657252,0.0004685686,0.0006440492,0.000702895,0.00002508678,0.0008058012],"genre_scores_gemma":[0.9018016,0.001609677,0.09357234,0.0002733716,0.0004608182,0.00002647018,0.001628034,0.00006378695,0.0005638641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6434222,"threshold_uncertainty_score":0.9999227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0391647357823615,"score_gpt":0.3427590313263774,"score_spread":0.3035942955440158,"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."}}