{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02398724,0.002104832,0.001573565,0.00451464,0.001764913,0.004151228,0.002652801,0.003799085,0.003514618],"category_scores_gemma":[0.05901636,0.0007598426,0.001797737,0.002654379,0.001112192,0.006020967,0.003575782,0.001960436,0.004922879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001646796,"about_ca_system_score_gemma":0.002561525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01350959,"about_ca_topic_score_gemma":0.01422165,"domain_scores_codex":[0.9866006,0.005560297,0.001512812,0.003984747,0.001841777,0.0004997181],"domain_scores_gemma":[0.9370123,0.04781562,0.001634399,0.004944483,0.007969134,0.0006240102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004308755,0.001472209,0.04825588,0.004318148,0.001918878,0.001757907,0.004870414,0.06441601,0.02778125,0.00461229,0.1000412,0.7362471],"study_design_scores_gemma":[0.001019066,0.00249049,0.09520957,0.001469107,0.002640327,0.004274985,0.004638851,0.6154096,0.1359269,0.01762785,0.1184446,0.0008488257],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5338311,0.01130611,0.2579353,0.004117094,0.002403591,0.002079219,0.03114495,0.1252758,0.03190691],"genre_scores_gemma":[0.5972087,0.002158368,0.324671,0.001446985,0.0003861718,0.000678673,0.05942224,0.004919535,0.009108316],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02398724,"threshold_uncertainty_score":0.1268581,"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."}}