{"id":"W4224037213","doi":"10.1101/2022.04.13.22273750","title":"Mondo: Unifying diseases for the world, by the world","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bioinformatics Solutions (Canada); Jewish General Hospital","funders":"U.S. National Library of Medicine; NIH Office of the Director; National Human Genome Research Institute; National Institutes of Health","keywords":"Ontology; Disease; Computer science; Data science; Key (lock); Data integration; Medicine; Data mining; Computer security","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":[0.002309491,0.0009288791,0.0006533263,0.005239013,0.001431588,0.004207583,0.001108475,0.001008573,0.01183313],"category_scores_gemma":[0.00837669,0.0004331892,0.002146503,0.004147406,0.0009151904,0.004544364,0.006426963,0.001486044,0.003834856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001235627,"about_ca_system_score_gemma":0.002704202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006959928,"about_ca_topic_score_gemma":0.008954654,"domain_scores_codex":[0.998848,0.0002756971,0.0001590262,0.0002877163,0.0003438932,0.00008569832],"domain_scores_gemma":[0.9987171,0.0004005014,0.0001575505,0.0003807537,0.0001572731,0.0001869514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003692828,0.00007237049,0.008965746,0.001404937,0.0002223201,0.0007754053,0.001544647,0.004618558,0.002862897,0.5237163,0.255147,0.2003005],"study_design_scores_gemma":[0.00005189336,0.00001858223,0.00300491,0.0004808433,0.00008431224,0.0006183095,0.0005596919,0.01519527,0.001194371,0.2004427,0.7782974,0.00005191059],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02131935,0.006933657,0.7444338,0.01381211,0.002733594,0.0007535268,0.1279187,0.02882764,0.05326761],"genre_scores_gemma":[0.1192881,0.005370975,0.7309968,0.002209052,0.0006502354,0.0006950279,0.1260492,0.003677523,0.01106294],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01183313,"threshold_uncertainty_score":0.03958577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02958830550962989,"score_gpt":0.312161597043262,"score_spread":0.2825732915336321,"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."}}