{"id":"W3118695267","doi":"10.3390/metabo11010044","title":"Comprehensive Meta-Analysis of COVID-19 Global Metabolomics Datasets","year":2021,"lang":"en","type":"article","venue":"Metabolites","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Cancer Institute; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Génome Québec; Genome Canada","keywords":"Context (archaeology); Metabolomics; Pandemic; Disease; Computational biology; Meta-analysis; Coronavirus disease 2019 (COVID-19); Biology; Computer science; Data science; Bioinformatics; Medicine; Infectious disease (medical specialty); Pathology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000338213,0.0003187103,0.001650607,0.0001550549,0.0001103047,0.00003682166,0.0003412039,0.000127924,0.0005718757],"category_scores_gemma":[0.0006933102,0.0002691199,0.001522062,0.001449,0.0001769323,0.000006880935,0.0004660531,0.00007782086,0.000006993075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001502584,"about_ca_system_score_gemma":0.0002204061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001000399,"about_ca_topic_score_gemma":0.0001685464,"domain_scores_codex":[0.9977971,0.0003007096,0.0005418917,0.0007133703,0.0002901343,0.0003567981],"domain_scores_gemma":[0.9980392,0.00007868071,0.0002670765,0.001053101,0.0003327329,0.0002292302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000971269,0.0003150393,0.005403037,0.00008146124,0.4443714,0.0000261611,0.00005365173,0.0008512518,0.511449,0.02874262,0.008373881,0.0002353753],"study_design_scores_gemma":[0.0004312567,0.00003620621,0.004916978,2.347126e-7,0.1695431,0.00001273223,0.0002012907,0.00003292876,0.1494402,0.0007391706,0.6743363,0.0003095932],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.4491156,0.5129316,0.0098597,0.001565557,0.0004337819,0.0003696505,0.02374305,0.00004224991,0.001938823],"genre_scores_gemma":[0.9381454,0.01509463,0.02739456,0.006218314,0.0002320222,0.0000853044,0.01174924,0.0000466856,0.001033861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6659625,"threshold_uncertainty_score":0.9999761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06276099500401609,"score_gpt":0.3401316694453377,"score_spread":0.2773706744413216,"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."}}