{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00137797,0.001204744,0.0008167968,0.001545828,0.0004482765,0.0006658999,0.0007184001,0.000952477,0.0009067195],"category_scores_gemma":[0.000721298,0.0006039623,0.0006948722,0.0008630012,0.0004289252,0.000636912,0.0005013057,0.001340577,0.00064624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004585554,"about_ca_system_score_gemma":0.0006295026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006641036,"about_ca_topic_score_gemma":0.001587226,"domain_scores_codex":[0.999228,0.0001276492,0.0000551585,0.0002470576,0.0002596686,0.0000824442],"domain_scores_gemma":[0.9993899,0.00008604029,0.000171239,0.00008263555,0.0001736067,0.00009669748],"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":[0.00007610722,0.00002490385,0.00021308,0.00003392875,0.00001374462,0.00002127443,0.00001070029,0.00005036548,0.9975897,0.00008726851,0.00005959993,0.001819239],"study_design_scores_gemma":[0.00002298775,0.0003485918,0.003569335,0.00001236059,0.00005004279,0.000263518,0.00001392923,0.001797303,0.9897358,0.0001900928,0.003971495,0.00002455817],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3393049,0.003456513,0.6397533,0.0005875396,0.0002440025,0.001195373,0.009018646,0.004233167,0.002206543],"genre_scores_gemma":[0.3225948,0.005125831,0.6520392,0.0006677758,0.0001127306,0.003655243,0.008548973,0.0009585966,0.006296837],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001545828,"threshold_uncertainty_score":0.007287502,"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."}}