{"id":"W4313534877","doi":"10.1371/journal.pone.0252002","title":"Machine learning algorithms can predict tail biting outbreaks in pigs using feeding behaviour records","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Society of Endocrinology and Metabolism","keywords":"Machine learning; Artificial intelligence; Algorithm; Support vector machine; Biting; Data set; Linear discriminant analysis; Computer science; Random forest; Biology; Ecology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004540124,0.0002640114,0.0004387679,0.0003272432,0.0004008936,0.00005492094,0.0001685948,0.0001177788,0.0001372189],"category_scores_gemma":[0.0002165004,0.0002804956,0.00008611552,0.0006113226,0.00005338768,0.0001558898,0.0003460442,0.0006108448,0.00006378785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001781096,"about_ca_system_score_gemma":0.00002807141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004955079,"about_ca_topic_score_gemma":0.0002111683,"domain_scores_codex":[0.9979725,0.0001002321,0.0004345193,0.0004424014,0.0004294577,0.0006208725],"domain_scores_gemma":[0.9994313,0.00008767284,0.0001336618,0.0001630894,0.00008524298,0.00009901557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005803708,0.0002494967,0.9516634,0.00007296328,0.00006966549,0.0004440473,0.001722681,0.00001407876,0.04377887,0.000007472225,0.00001941819,0.001899903],"study_design_scores_gemma":[0.0007772236,0.0008622485,0.9736319,0.000733691,0.000260382,0.00003796203,0.002550907,0.01990422,0.00065946,0.00003294178,0.00002422455,0.0005248985],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973736,0.0001363017,0.00001592309,0.000193174,0.0001124634,0.0002514707,0.000100404,0.0005521735,0.001264516],"genre_scores_gemma":[0.9977238,0.00008876389,0.001064793,0.00002281921,0.0002712463,0.00005295333,0.00008384237,0.00008513336,0.0006066827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0431194,"threshold_uncertainty_score":0.9999647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1516820391890098,"score_gpt":0.3193120291444242,"score_spread":0.1676299899554144,"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."}}