{"id":"W2956092424","doi":"10.3390/metabo9060108","title":"Translational Metabolomics: Current Challenges and Future Opportunities","year":2019,"lang":"en","type":"article","venue":"Metabolites","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":211,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Metabolomics; Translational research; Data science; Session (web analytics); Identification (biology); Biomarker discovery; Computer science; Translational science; Computational biology; Bioinformatics; Biology; Medicine; Proteomics; Biotechnology; World Wide Web; Ecology; Pathology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.04970998,0.001420601,0.002632526,0.001850627,0.002862725,0.01181371,0.004215818,0.01278339,0.01442305],"category_scores_gemma":[0.03248418,0.0007416257,0.002057977,0.002640894,0.01000129,0.02500134,0.01030719,0.01300162,0.003999192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003525902,"about_ca_system_score_gemma":0.01642128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00195951,"about_ca_topic_score_gemma":0.002886865,"domain_scores_codex":[0.9899593,0.004481568,0.0009000165,0.001302647,0.002370461,0.0009860554],"domain_scores_gemma":[0.927907,0.04831057,0.002649033,0.002383532,0.01192291,0.006827028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000569531,0.0003868086,0.002168664,0.01120391,0.0001403963,0.0009221837,0.001719432,0.002330878,0.002675672,0.1344575,0.1544008,0.6890243],"study_design_scores_gemma":[0.00008156292,0.0004625162,0.002169916,0.007826674,0.0001066466,0.001586125,0.007537617,0.0029956,0.001110716,0.311904,0.6639772,0.0002414507],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.001807865,0.4658103,0.01284908,0.5072983,0.007492221,0.000069084,0.0001868263,0.0002573238,0.004229015],"genre_scores_gemma":[0.0361164,0.8118494,0.04419217,0.07468146,0.0275772,0.0003804087,0.0006197568,0.0001868196,0.004396268],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.04970998,"threshold_uncertainty_score":0.2628946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02991001479710622,"score_gpt":0.2535947031511933,"score_spread":0.223684688354087,"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."}}