{"id":"W3087687880","doi":"10.3390/metabo10090376","title":"Towards Standardization of Data Normalization Strategies to Improve Urinary Metabolomics Studies by GC×GC-TOFMS","year":2020,"lang":"en","type":"article","venue":"Metabolites","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Genome Alberta; Canada Foundation for Innovation; Genome Canada","keywords":"Normalization (sociology); Metabolomics; Creatinine; Urine; Principal component analysis; Database normalization; Urinary system; Chemistry; Chromatography; Computer science; Data mining; Pattern recognition (psychology); Artificial intelligence; Internal medicine; Medicine","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03953261,0.002829403,0.001740222,0.005283992,0.001225413,0.003361324,0.002669424,0.001826405,0.00164162],"category_scores_gemma":[0.04054711,0.0009831318,0.001632976,0.004563176,0.002115595,0.003409479,0.003620906,0.003128015,0.001746087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001064533,"about_ca_system_score_gemma":0.004185196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002244233,"about_ca_topic_score_gemma":0.003879621,"domain_scores_codex":[0.9780091,0.007776751,0.001784527,0.004223963,0.007582938,0.0006226838],"domain_scores_gemma":[0.986668,0.003302327,0.00133817,0.002099135,0.006358413,0.0002338934],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007258975,0.0004786471,0.01358468,0.002407005,0.0006885597,0.0002867612,0.00108,0.005390977,0.6495741,0.008042332,0.004582885,0.3131582],"study_design_scores_gemma":[0.0001120095,0.001138914,0.0345575,0.0007999962,0.0006726754,0.0009454082,0.0007205997,0.05788479,0.8124775,0.01437642,0.07585251,0.0004618348],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03746379,0.006036818,0.9480987,0.0007923673,0.0005382713,0.000937629,0.001171731,0.002788314,0.002172393],"genre_scores_gemma":[0.07043077,0.003389062,0.9205558,0.0006377716,0.0001736881,0.001285015,0.001976739,0.0006844128,0.0008667897],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9604674,"threshold_uncertainty_score":0.2090709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03540160863402068,"score_gpt":0.3153520658598419,"score_spread":0.2799504572258212,"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."}}