{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00124801,0.00009313702,0.0002211839,0.00003939403,0.0001035728,0.00004193131,0.0004372331,0.0001461043,0.000009236489],"category_scores_gemma":[0.00539841,0.00005918817,0.0001512304,0.0002656968,0.0002439974,0.00001833977,0.0001625425,0.0002560717,0.0000168041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001200317,"about_ca_system_score_gemma":0.0002453737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001740879,"about_ca_topic_score_gemma":0.000009617192,"domain_scores_codex":[0.9981573,0.0001153238,0.0005840285,0.00006180158,0.0008561817,0.0002254027],"domain_scores_gemma":[0.9981838,0.00007466457,0.001129556,0.0001999415,0.0002783654,0.0001335972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006202712,0.00032317,0.04791931,0.00009057721,0.001169354,0.00001041407,0.007455293,0.000187425,0.0290705,0.0003105882,0.6174226,0.2954205],"study_design_scores_gemma":[0.001730263,0.004068523,0.06119382,0.0003292328,0.0001992336,0.0001518915,0.003077023,0.001785859,0.01950217,0.001190157,0.9062303,0.000541581],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9295296,0.00008813161,0.05798277,0.01066533,0.0009374274,0.0000767153,0.000007162947,0.000007768844,0.0007050748],"genre_scores_gemma":[0.9772098,0.0002556554,0.009775049,0.009942459,0.002489491,0.000002370032,0.000006596267,0.00001063817,0.0003079533],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2948789,"threshold_uncertainty_score":0.6462792,"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."}}