{"id":"W2043419849","doi":"10.3166/ria.18.111-137","title":"L'UMLS entre langue et ontologie : une approche pragmatique dans le domaine médical","year":2004,"lang":"fr","type":"article","venue":"Revue d intelligence artificielle","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Unified Medical Language System; Philosophy; Linguistics; Humanities; Computer science; Natural language processing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01150199,0.0009879228,0.0009229981,0.003509682,0.001816858,0.007420424,0.00175572,0.002849674,0.003991516],"category_scores_gemma":[0.0146236,0.001094257,0.002765987,0.002708743,0.004190149,0.01119364,0.004594755,0.004265031,0.002088981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002353056,"about_ca_system_score_gemma":0.004587438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008312401,"about_ca_topic_score_gemma":0.007944053,"domain_scores_codex":[0.9923003,0.003665595,0.001080458,0.0009555675,0.001783887,0.0002142651],"domain_scores_gemma":[0.9927151,0.004032908,0.00052177,0.001311003,0.001150447,0.0002688263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000135432,0.00008233865,0.00133584,0.0009314771,0.0001228611,0.0005858516,0.007619327,0.005590704,0.008726346,0.7001807,0.01465504,0.2600341],"study_design_scores_gemma":[0.00006166012,0.00007997552,0.0007110526,0.0008294646,0.000153892,0.001210234,0.001244879,0.04248204,0.008150956,0.3498201,0.5951303,0.0001254432],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002129248,0.001147893,0.9849214,0.003915031,0.0002257594,0.0001095047,0.0002407227,0.00247106,0.004839351],"genre_scores_gemma":[0.04225901,0.002026023,0.9463887,0.00139736,0.0002140411,0.0002210363,0.0006662458,0.0007072178,0.006120407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01150199,"threshold_uncertainty_score":0.06082904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02777079375492197,"score_gpt":0.2925595196639217,"score_spread":0.2647887259089997,"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."}}