{"id":"W1972357547","doi":"10.1021/pr500828v","title":"Development of Isotope Labeling Liquid Chromatography Mass Spectrometry for Mouse Urine Metabolomics: Quantitative Metabolomic Study of Transgenic Mice Related to Alzheimer’s Disease","year":2014,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"Canadian Institutes of Health Research; Genome Alberta; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation","keywords":"Metabolomics; Metabolome; Urine; Metabolite; Chromatography; Chemistry; Sample preparation; Liquid chromatography–mass spectrometry; Repeatability; Biomarker discovery; Mass spectrometry; Biomarker; Proteomics; Biochemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.005054544,0.0002620576,0.0008853896,0.001172986,0.0001671827,0.00002202931,0.0006340531,0.0001076283,0.00001664521],"category_scores_gemma":[0.0008925195,0.0002186015,0.0003306883,0.0009722204,0.0001316134,0.00001776536,0.0001894645,0.0003427647,0.000002080474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000294091,"about_ca_system_score_gemma":0.0003337154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009346501,"about_ca_topic_score_gemma":0.00001594375,"domain_scores_codex":[0.996498,0.0004656524,0.00134613,0.0004210106,0.0007468431,0.0005223346],"domain_scores_gemma":[0.9971501,0.0001416981,0.000622808,0.0004053138,0.001398149,0.000281927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003983115,0.0008134542,0.0004022735,0.0001349669,0.002184903,0.000001584927,0.0007185155,0.00008359238,0.9909815,0.0004595929,0.00006896519,0.0001675549],"study_design_scores_gemma":[0.002945719,0.007131832,0.001894664,0.00005199775,0.0003121715,0.000004065901,0.00168161,0.00004333677,0.9831257,0.0002720726,0.002285008,0.000251768],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985511,0.006190712,0.006165555,0.0001719909,0.0001201807,0.001761536,0.00003956972,0.000004427641,0.00003501321],"genre_scores_gemma":[0.887422,0.0006843435,0.1115655,0.00001065714,0.00009032593,0.0001162583,0.000008773829,0.00004491755,0.00005722589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1053999,"threshold_uncertainty_score":0.891431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05580779035570752,"score_gpt":0.3657816501923067,"score_spread":0.3099738598365992,"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."}}