{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0110485,0.002173014,0.003922587,0.008666018,0.000800127,0.002255702,0.001276334,0.0009051217,0.001607963],"category_scores_gemma":[0.01452301,0.0006014208,0.01320206,0.01063717,0.0003850233,0.0008511195,0.002639053,0.001152976,0.0002630283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001140209,"about_ca_system_score_gemma":0.002625013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00833536,"about_ca_topic_score_gemma":0.009982208,"domain_scores_codex":[0.9933249,0.003578818,0.0006583969,0.00146718,0.0006744589,0.0002962642],"domain_scores_gemma":[0.9912285,0.005441375,0.0008636108,0.001515566,0.0006764052,0.0002746004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.003951324,0.0001996782,0.3897023,0.01593168,0.484991,0.0009706971,0.0003128929,0.03031261,0.009492983,0.00224693,0.0108079,0.05108005],"study_design_scores_gemma":[0.000723814,0.0008612662,0.5197183,0.00297313,0.375995,0.001376225,0.0007380534,0.02852251,0.007205238,0.01058339,0.0508624,0.0004406879],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4943524,0.1927764,0.06025168,0.004194418,0.0006610098,0.0005166255,0.2423109,0.001895863,0.003040568],"genre_scores_gemma":[0.8511596,0.01543568,0.03204156,0.0008926206,0.0001700768,0.0005773017,0.09883419,0.0003755334,0.0005134005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0110485,"threshold_uncertainty_score":0.05843073,"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."}}