{"id":"W4415185722","doi":"10.1016/j.prevetmed.2025.106723","title":"Automating classification of veterinary biosecurity recommendations using machine learning","year":2025,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Association des Médecins Vétérinaires du Québec; University of Guelph; University of Calgary; Université de Montréal; Cegep de Saint Hyacinthe","funders":"Ville de Québec; Novalait; Université de Montréal; Dairy Farmers of Canada; Natural Sciences and Engineering Research Council of Canada; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation","keywords":"Biosecurity; Random forest; Support vector machine; Naive Bayes classifier; Consistency (knowledge bases); Linear discriminant analysis; Bayes' theorem; Quality assurance","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006426102,0.0001604522,0.0002647223,0.0004699724,0.0002177801,0.00002459768,0.0006471566,0.00007581134,0.00005916575],"category_scores_gemma":[0.0003539669,0.0001468475,0.00005979031,0.0009160635,0.0001844539,0.0004546176,0.0004734313,0.000216447,0.000004342192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008827638,"about_ca_system_score_gemma":0.00005064505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004063885,"about_ca_topic_score_gemma":0.000001022417,"domain_scores_codex":[0.9984321,0.0003022658,0.000527443,0.0003671758,0.000182298,0.0001886973],"domain_scores_gemma":[0.9987096,0.0002548245,0.0003833002,0.0004941371,0.0001231542,0.00003497333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004045148,0.0002622875,0.007842332,0.0002905458,0.0001027755,0.000009979629,0.00115912,0.00001095586,0.6805987,0.07147564,0.0003311082,0.2378761],"study_design_scores_gemma":[0.003652715,0.005421416,0.2681641,0.003795125,0.0001880435,0.0001533972,0.002515675,0.5954555,0.03695484,0.03283732,0.04982619,0.001035668],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5439494,0.0009636655,0.4462609,0.005762533,0.0005552084,0.0003465665,0.000004818849,0.00059379,0.001563136],"genre_scores_gemma":[0.9683763,0.0001429755,0.03102885,0.00004651222,0.00001995305,0.00003080591,0.00002450209,0.000006795896,0.0003233172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6436439,"threshold_uncertainty_score":0.5988266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09194989337965144,"score_gpt":0.3794110015846574,"score_spread":0.287461108205006,"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."}}