{"id":"W4412676072","doi":"10.1016/j.prevetmed.2025.106630","title":"Evaluating machine learning classifiers and explainability for monitoring cow behaviour with wearable nose rings","year":2025,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Wearable computer; Electronic nose; Nose; Artificial intelligence; Computer science; Machine learning; Human–computer interaction; Pattern recognition (psychology); Computer vision; Medicine; Anatomy; Embedded system","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001071744,0.0003392865,0.0005206527,0.0001587073,0.0006635988,0.00002423211,0.0001497662,0.00008330803,0.00005182308],"category_scores_gemma":[0.0004198765,0.0002696846,0.00007566879,0.0002275407,0.0003415035,0.0002057081,0.0002616113,0.0003592838,0.00000185725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001331584,"about_ca_system_score_gemma":0.00003676848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002437772,"about_ca_topic_score_gemma":0.000004741858,"domain_scores_codex":[0.9980187,0.0002606826,0.0003959384,0.0006313543,0.0002616086,0.0004316887],"domain_scores_gemma":[0.9988677,0.0004200912,0.0001601898,0.0002352456,0.0002161217,0.000100687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005130941,0.0001876684,0.8884466,0.0007481751,0.000216937,0.0001688285,0.002611599,0.000008755401,0.07959811,0.0001654406,0.0000388025,0.02267816],"study_design_scores_gemma":[0.003691442,0.0187989,0.9668886,0.001988313,0.0005037252,0.0001473081,0.0052855,0.0005576081,0.0005167658,0.0002831214,0.0009476758,0.0003910669],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927098,0.002944559,0.001379011,0.0005163167,0.0002529423,0.0009864165,0.00001341239,0.0001409419,0.001056603],"genre_scores_gemma":[0.995734,0.0001563142,0.002033425,0.00002489371,0.0001013007,0.0004773847,0.00001034487,0.00003562639,0.001426666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07908134,"threshold_uncertainty_score":0.9999756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1361437283257485,"score_gpt":0.4386961043329811,"score_spread":0.3025523760072325,"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."}}