{"id":"W2142923621","doi":"10.1007/s11306-009-0168-0","title":"Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research","year":2009,"lang":"en","type":"article","venue":"Metabolomics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":567,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Institute of Environmental Health Sciences; National Institute of Diabetes and Digestive and Kidney Diseases; National Cancer Institute; National Institute on Aging","keywords":"Metabolomics; Metabolome; Limiting; Identification (biology); Biochemical engineering; Mass spectrometry; Computer science; Computational biology; Data science; Biotechnology; Bioinformatics; Chemistry; Biology; Chromatography; Engineering","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":[0.0008166262,0.0003017624,0.0004011485,0.0003437314,0.0004878816,0.0001197398,0.0002123452,0.0001949354,0.000007889526],"category_scores_gemma":[0.0002082349,0.0002543024,0.0001327484,0.0005040207,0.0001950318,0.00001519066,0.00003613271,0.0002641327,0.000004148414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003278676,"about_ca_system_score_gemma":0.00008831775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001814603,"about_ca_topic_score_gemma":0.00001873898,"domain_scores_codex":[0.9979512,0.0001800521,0.0003260724,0.0006933044,0.0002625721,0.0005867944],"domain_scores_gemma":[0.9986662,0.0001196244,0.000137378,0.0005018147,0.0004247565,0.0001502209],"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":[0.00354791,0.002585261,0.001907706,0.0001235571,0.00108122,0.000009731488,0.0002782002,0.0001361932,0.5675477,0.3571322,0.01610224,0.04954811],"study_design_scores_gemma":[0.007642125,0.007145413,0.008349672,0.00005776361,0.0004710882,0.00001903114,0.001602447,0.0007786794,0.4500845,0.03102774,0.4916827,0.001138869],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8384578,0.02408405,0.0596922,0.07159309,0.0005830179,0.003856609,0.0005791963,0.0001150696,0.001038968],"genre_scores_gemma":[0.7923014,0.005511144,0.1998277,0.000575481,0.0006796379,0.0005464158,0.0004024067,0.00004511507,0.0001106814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4755805,"threshold_uncertainty_score":0.9999909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04147571255590651,"score_gpt":0.3184582230425517,"score_spread":0.2769825104866452,"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."}}