{"id":"W2049645944","doi":"10.1016/j.jbi.2012.02.012","title":"Lexical patterns, features and knowledge resources for coreference resolution in clinical notes","year":2012,"lang":"en","type":"article","venue":"Journal of Biomedical Informatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"U.S. National Library of Medicine","keywords":"Coreference; Computer science; Natural language processing; Artificial intelligence; Baseline (sea); Measure (data warehouse); Resolution (logic); Variety (cybernetics); Recall; Precision and recall; Information retrieval; Data mining; Linguistics","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":[0.002428724,0.0003972623,0.0007129411,0.007181103,0.001311045,0.002490566,0.001054611,0.001026423,0.004712162],"category_scores_gemma":[0.01451682,0.0003441655,0.0008602607,0.004535828,0.0006136154,0.003695864,0.00209939,0.0009479094,0.001133002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007981414,"about_ca_system_score_gemma":0.001988606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003496313,"about_ca_topic_score_gemma":0.006019255,"domain_scores_codex":[0.9976932,0.0006837203,0.0006397017,0.0003481261,0.0004806569,0.0001544853],"domain_scores_gemma":[0.9903637,0.00721402,0.0005985625,0.0006621422,0.0009274795,0.0002341106],"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":[0.002333673,0.0009112032,0.06531604,0.002476199,0.0004532024,0.004349694,0.003491898,0.00724195,0.03798751,0.03109028,0.02936958,0.8149787],"study_design_scores_gemma":[0.0008619178,0.0009762311,0.1387313,0.00320815,0.003183502,0.01674973,0.01641947,0.3938424,0.1029219,0.1871049,0.1355014,0.0004992119],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5658287,0.004514576,0.3640148,0.00425024,0.0002972266,0.001399377,0.03666746,0.006325266,0.0167024],"genre_scores_gemma":[0.7340262,0.0006855921,0.2410001,0.0003003139,0.00007462027,0.0005517605,0.02128878,0.0002969372,0.001775725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007181103,"threshold_uncertainty_score":0.01576376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04965589118374901,"score_gpt":0.3658778250269115,"score_spread":0.3162219338431625,"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."}}