{"id":"W2892602486","doi":"10.1016/j.aca.2018.09.035","title":"Quantitative determination of potential urine biomarkers of respiratory illnesses using new targeted metabolomic approach","year":2018,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Food Inspection Agency; University of Saskatchewan","funders":"Western Economic Diversification Canada; AllerGen; Saskatchewan Health Research Foundation","keywords":"Chemistry; Metabolomics; Hydrophilic interaction chromatography; Chromatography; Ion suppression in liquid chromatography–mass spectrometry; Selected reaction monitoring; Biomarker; Biomarker discovery; Tandem mass spectrometry; Mass spectrometry; Proteomics; High-performance liquid chromatography; 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":[0.0003608963,0.000575408,0.0005480583,0.0008952948,0.0002431104,0.0003943456,0.0002819005,0.000594597,0.0003819304],"category_scores_gemma":[0.0004421887,0.0002149937,0.0003817047,0.0005412627,0.0002129964,0.0002867174,0.0003530101,0.0004031286,0.0001622835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001894307,"about_ca_system_score_gemma":0.0003074979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005269367,"about_ca_topic_score_gemma":0.001293861,"domain_scores_codex":[0.9996259,0.00009009807,0.0000159324,0.000117209,0.0001143497,0.00003643292],"domain_scores_gemma":[0.9998554,0.00003154416,0.00003511175,0.00001094221,0.00005055859,0.0000164479],"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":[0.0003390337,0.00008217222,0.006766107,0.0001239507,0.0001163061,0.00009214453,0.00004229549,0.0002743236,0.9772751,0.000113314,0.00009183477,0.0146835],"study_design_scores_gemma":[0.00003807798,0.001356119,0.04568042,0.00002598166,0.0003554778,0.001405938,0.0001272258,0.01207179,0.9360526,0.0004271106,0.002403143,0.00005611771],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8427226,0.01197915,0.1402311,0.0003144047,0.0002132144,0.0002539223,0.001864202,0.0003685514,0.002052955],"genre_scores_gemma":[0.9261975,0.003246153,0.06727765,0.0003660964,0.00008554904,0.0002467722,0.0007203732,0.00002189848,0.0018379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008952948,"threshold_uncertainty_score":0.0019086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02518646364517645,"score_gpt":0.2906551128765322,"score_spread":0.2654686492313557,"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."}}