{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001463788,0.0008441291,0.0005223831,0.002756578,0.000460003,0.001313889,0.0007153125,0.0008439244,0.001304295],"category_scores_gemma":[0.005208849,0.0001490544,0.0005362672,0.001526192,0.0001933914,0.0009043408,0.0002716818,0.0007098638,0.001503491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001578247,"about_ca_system_score_gemma":0.002259904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07383468,"about_ca_topic_score_gemma":0.09377553,"domain_scores_codex":[0.999051,0.0002357238,0.0001176727,0.0002742187,0.0002253131,0.00009603307],"domain_scores_gemma":[0.9957603,0.002233108,0.0003323182,0.0002112133,0.001391463,0.00007160449],"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":[0.0002963469,0.0004189153,0.05246766,0.0005918438,0.0001214325,0.0003931073,0.0004789961,0.02940882,0.01587973,0.0006743927,0.02526246,0.8740065],"study_design_scores_gemma":[0.00005446213,0.0002649211,0.05231015,0.0002651607,0.000127297,0.0003166909,0.0008691048,0.8989674,0.02143154,0.001892531,0.02343853,0.00006212479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6650146,0.005515546,0.2774745,0.002573788,0.0005277297,0.00163436,0.0215816,0.01553632,0.01014161],"genre_scores_gemma":[0.739572,0.001028373,0.2254326,0.0003153258,0.000124353,0.0003872386,0.02556171,0.0001186157,0.007459801],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.07383468,"threshold_uncertainty_score":0.1468098,"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."}}