{"id":"W4405727497","doi":"10.1111/vco.13035","title":"Precision in Parsing: Evaluation of an Open‐Source Named Entity Recognizer (<scp>NER</scp>) in Veterinary Oncology","year":2024,"lang":"en","type":"article","venue":"Veterinary and Comparative Oncology","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Guelph; Oakville-Trafalgar Memorial Hospital; Health Sciences Centre; Sunnybrook Health Science Centre","funders":"","keywords":"Jaccard index; Named-entity recognition; F1 score; Precision and recall; Medicine; Computer science; Veterinary medicine; Artificial intelligence","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":[0.003013797,0.0002004481,0.0005813154,0.0003905763,0.0000700239,0.0001116435,0.0007628949,0.0002080602,0.00001931373],"category_scores_gemma":[0.0001063439,0.0001983463,0.00003552291,0.000531614,0.0001283575,0.001100194,0.0009158883,0.0003595306,0.00001222161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003969273,"about_ca_system_score_gemma":0.0004575177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001872056,"about_ca_topic_score_gemma":0.000181648,"domain_scores_codex":[0.9956623,0.002368819,0.0006014691,0.0007844685,0.0002630054,0.0003199147],"domain_scores_gemma":[0.9983585,0.0008882196,0.000150112,0.0003941381,0.0001147236,0.00009426098],"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":[0.000350931,0.001323239,0.0006244319,0.0001047523,0.00004641465,0.0005716594,0.02648644,0.005139635,0.03321296,0.004080037,0.0002038034,0.9278557],"study_design_scores_gemma":[0.00211447,0.01161938,0.01016093,0.0002470308,0.00002224296,0.0005517612,0.00119886,0.9068371,0.0006827521,0.007120292,0.05927444,0.0001707563],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9751607,0.0009707817,0.01479209,0.0001794868,0.0006794808,0.0006771241,0.000002933684,0.00004845628,0.007489002],"genre_scores_gemma":[0.9853025,0.00004835151,0.01422941,0.0000784146,0.00004532023,0.00014873,0.000008938614,0.000008458874,0.0001298954],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.927685,"threshold_uncertainty_score":0.8088328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2758151808341951,"score_gpt":0.4448319689970707,"score_spread":0.1690167881628756,"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."}}