{"id":"W2973398987","doi":"10.1186/s13326-019-0207-3","title":"Automated SNOMED CT concept and attribute relationship detection through a web-based implementation of cTAKES","year":2019,"lang":"en","type":"article","venue":"Journal of Biomedical Semantics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"European Regional Development Fund","keywords":"SNOMED CT; Computer science; Information retrieval; World Wide Web; Data science; Data mining; Terminology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003751786,0.0001017443,0.0002383074,0.00008076382,0.00003916014,0.00001189846,0.0001081388,0.0001417243,0.00003163561],"category_scores_gemma":[0.0002518857,0.00007812351,0.00008709048,0.0001730134,0.0002582551,0.000007306895,0.00003313686,0.0001399924,0.00000185651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001584194,"about_ca_system_score_gemma":0.0001416151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001118565,"about_ca_topic_score_gemma":0.000007786542,"domain_scores_codex":[0.9988453,0.00008751552,0.0004988273,0.0001274637,0.0002864072,0.000154488],"domain_scores_gemma":[0.9991482,0.00009924493,0.0004200855,0.0001129278,0.0001300201,0.00008945304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002230206,0.0002606148,0.1165085,0.0002525574,0.0003129544,0.00003162195,0.0004054728,0.00003213585,0.8234475,0.00005879599,0.004948679,0.05351818],"study_design_scores_gemma":[0.0131685,0.00813648,0.3083479,0.0003973333,0.000334999,0.0005298299,0.003427999,0.005285616,0.55729,0.0003435518,0.1021433,0.0005945764],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889517,0.0006352649,0.009041217,0.0009209391,0.0003083601,0.00009114885,0.00002203088,0.00001742162,0.00001189617],"genre_scores_gemma":[0.9965681,0.00009224496,0.003052713,0.0001078921,0.0001104848,0.000001054401,0.0000397623,0.00000841602,0.00001933525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2661576,"threshold_uncertainty_score":0.3185784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01663212151399014,"score_gpt":0.309345508240613,"score_spread":0.2927133867266229,"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."}}