{"id":"W2002114056","doi":"10.1002/elps.201400604","title":"Multiplexed separations for biomarker discovery in metabolomics: Elucidating adaptive responses to exercise training","year":2015,"lang":"en","type":"article","venue":"Electrophoresis","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metabolomics; Hypoxanthine; Metabolome; Biomarker discovery; Chemistry; Internal medicine; Medicine; Proteomics; Biochemistry; Chromatography","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":[0.001797692,0.001109784,0.0007747702,0.001370192,0.0004595459,0.001438935,0.0007537057,0.001068125,0.0009249116],"category_scores_gemma":[0.001894248,0.0005283386,0.0004512467,0.0008954761,0.0006029075,0.0008872772,0.0009603272,0.001037645,0.0006324277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000446846,"about_ca_system_score_gemma":0.0008006502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001023273,"about_ca_topic_score_gemma":0.002461022,"domain_scores_codex":[0.9990342,0.0001513019,0.00005467774,0.0003760508,0.0003091248,0.00007461113],"domain_scores_gemma":[0.9990935,0.0002932055,0.0002038308,0.0001032014,0.0002322629,0.00007405778],"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":[0.0003134186,0.0001380662,0.00371193,0.0002016478,0.00008746848,0.00006155473,0.0001037223,0.0004939898,0.9571958,0.0004355842,0.0003701678,0.03688661],"study_design_scores_gemma":[0.00005118748,0.000765197,0.01883242,0.00005979112,0.0001108717,0.0004577238,0.0001350519,0.01687137,0.9553538,0.001612545,0.005661624,0.00008838475],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4740264,0.01196432,0.5033994,0.001207311,0.0003277962,0.0008445815,0.003258241,0.002832602,0.002139292],"genre_scores_gemma":[0.4582516,0.009425836,0.5235408,0.0009978284,0.0002439957,0.001081732,0.001746302,0.0002410425,0.00447081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001797692,"threshold_uncertainty_score":0.009507239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06458161038500619,"score_gpt":0.3143578952583246,"score_spread":0.2497762848733184,"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."}}