{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002338996,0.0002048401,0.000298908,0.00008694999,0.00005893027,0.00002441655,0.000119849,0.00008160119,0.00009119389],"category_scores_gemma":[0.00001646699,0.0001735155,0.00009879368,0.00004593024,0.00006743556,0.000009101851,0.00008066335,0.0000897396,0.00001335347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002018249,"about_ca_system_score_gemma":0.00003360202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001255953,"about_ca_topic_score_gemma":0.000004219188,"domain_scores_codex":[0.9989905,0.00005906119,0.0001939977,0.0003769459,0.0001387436,0.0002407186],"domain_scores_gemma":[0.9995057,0.00001674244,0.00006746823,0.0002571665,0.00007377109,0.00007917326],"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":[0.0001078505,0.0001277149,0.005110271,0.0001547417,0.000482195,0.000001257479,0.0004735465,0.000002461157,0.2017745,0.3302467,0.0007371635,0.4607816],"study_design_scores_gemma":[0.000445592,0.00005545619,0.02652056,0.000003636382,0.00005399146,0.000006681869,0.0004776857,0.00000830995,0.01148624,0.001610515,0.9591076,0.0002237643],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"review","genre_scores_codex":[0.5002838,0.4928482,0.00002609977,0.001172477,0.0005797467,0.0001452473,0.0000459715,0.0000135417,0.004884906],"genre_scores_gemma":[0.4954011,0.4998758,0.001874316,0.0001950491,0.001462524,0.00003221361,0.0001300642,0.00003123741,0.0009976479],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9583704,"threshold_uncertainty_score":0.7075756,"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."}}