{"id":"W2084493006","doi":"10.1016/j.ijmedinf.2005.08.008","title":"Amplification of Terminologia anatomica by French language terms using Latin terms matching algorithm: A prototype for other language","year":2005,"lang":"en","type":"article","venue":"International Journal of Medical Informatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Hospitalier Universitaire de Sherbrooke","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Terminology; Computer science; Natural language processing; Linguistics; Information retrieval; Artificial intelligence; Philosophy","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":[0.0005241652,0.0000993756,0.0001769562,0.00008876537,0.00002226035,0.00002581024,0.0005431859,0.0002156407,0.00004777256],"category_scores_gemma":[0.0003634349,0.00007365671,0.00009668477,0.00003826172,0.0001223593,0.00001731582,0.00007790531,0.0001673684,0.00000173675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003003462,"about_ca_system_score_gemma":0.00009592486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002486714,"about_ca_topic_score_gemma":0.000005590066,"domain_scores_codex":[0.9985328,0.0000231861,0.0006904777,0.00006477005,0.0005532808,0.0001355391],"domain_scores_gemma":[0.9990579,0.00005021675,0.000551329,0.0001103782,0.0001431335,0.00008697408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001409012,0.000245757,0.00105208,0.0000827595,0.0003322126,0.000009623132,0.004351219,0.00005220146,0.1033665,0.00007079419,0.005658261,0.8846377],"study_design_scores_gemma":[0.01349147,0.002987769,0.002213425,0.001836557,0.0002297155,0.001528402,0.01311029,0.1527815,0.5820113,0.001021859,0.2274746,0.001313212],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7554698,0.0002691131,0.243127,0.0005722553,0.0002181378,0.0001337648,0.00008861241,0.000007685459,0.0001136019],"genre_scores_gemma":[0.8310235,0.00006767814,0.1673105,0.0007712843,0.000632728,0.000008641098,0.00008619466,0.00001359115,0.00008586382],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8833245,"threshold_uncertainty_score":0.3003634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01785284671722043,"score_gpt":0.344074210660259,"score_spread":0.3262213639430386,"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."}}