{"id":"W4531093","doi":"","title":"Extraction of Disease-Treatment Semantic Relations from Biomedical Sentences","year":2010,"lang":"en","type":"article","venue":"Meeting of the Association for Computational Linguistics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Relationship extraction; Relation (database); Semantic relation; Measure (data warehouse); Computer science; Natural language processing; Focus (optics); Artificial intelligence; Semantics (computer science); Information retrieval; Information extraction; Data mining; Medicine; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002777044,0.001859489,0.0009872274,0.007624525,0.001354645,0.001555713,0.0007805753,0.001448708,0.004522142],"category_scores_gemma":[0.01233124,0.0005195304,0.001780354,0.003762815,0.0005567627,0.002806606,0.001316085,0.001519204,0.002280975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009379207,"about_ca_system_score_gemma":0.002812756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001999721,"about_ca_topic_score_gemma":0.002263961,"domain_scores_codex":[0.9970175,0.001048125,0.0005859991,0.0006483123,0.0005976566,0.0001024062],"domain_scores_gemma":[0.9887217,0.007994466,0.001213963,0.0006097175,0.001276883,0.0001831773],"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":[0.001480084,0.001069118,0.033326,0.01049337,0.0007212496,0.005336471,0.004174273,0.009742117,0.1425313,0.0331117,0.04533693,0.7126773],"study_design_scores_gemma":[0.000577191,0.001261284,0.1126506,0.002509524,0.003137933,0.0133085,0.006601665,0.2140257,0.173768,0.1312646,0.3404145,0.0004804946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2316056,0.01289424,0.6067489,0.007939966,0.001553746,0.003058734,0.1017147,0.008703025,0.02578108],"genre_scores_gemma":[0.3063249,0.002589495,0.5997563,0.000817115,0.0006653175,0.0008578229,0.08667322,0.0002977442,0.002017991],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007624525,"threshold_uncertainty_score":0.01512808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01283156771914793,"score_gpt":0.2901257257106199,"score_spread":0.2772941579914719,"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."}}