{"id":"W2797078590","doi":"10.1093/jamia/ocy021","title":"UMLS to DBPedia link discovery through circular resolution","year":2018,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Unified Medical Language System; Computer science; Information retrieval; Annotation; Set (abstract data type); Simple Knowledge Organization System; Natural language processing; Ontology; Thesaurus; Artificial intelligence; Semantic Web; RDF; SPARQL","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":[0.01072973,0.001860399,0.001109037,0.0120532,0.003137649,0.006642663,0.00352215,0.001663428,0.006526298],"category_scores_gemma":[0.03908367,0.001496425,0.002878759,0.009357738,0.001510703,0.007344742,0.01117394,0.003362581,0.006056218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001777677,"about_ca_system_score_gemma":0.005700599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01913538,"about_ca_topic_score_gemma":0.02041634,"domain_scores_codex":[0.9876715,0.003998433,0.001318262,0.00300293,0.0035864,0.0004225342],"domain_scores_gemma":[0.9795257,0.007343664,0.001495003,0.006428531,0.004753102,0.0004540801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005086521,0.0004830738,0.00706533,0.00262459,0.0007922096,0.002038224,0.004877807,0.02766376,0.01203319,0.101608,0.1761732,0.6641319],"study_design_scores_gemma":[0.0001247304,0.00009552791,0.003525443,0.000987405,0.0004446152,0.001292449,0.003240546,0.2494546,0.05552881,0.1604954,0.5245087,0.0003018166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007386687,0.0008117158,0.9570989,0.0009760852,0.0003802781,0.0004430596,0.00720324,0.01662073,0.00907934],"genre_scores_gemma":[0.04573123,0.0007569898,0.9171794,0.0007810955,0.0001153179,0.0004232036,0.02780839,0.002219431,0.004984962],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01913538,"threshold_uncertainty_score":0.05674493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0104336042948062,"score_gpt":0.291618565749867,"score_spread":0.2811849614550608,"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."}}