{"id":"W4408179300","doi":"10.1016/j.compag.2025.110206","title":"Redefining lameness assessment: Constructing lameness hierarchy using crowd-sourced data","year":2025,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Boehringer Ingelheim Animal Health; Dairy Farmers of Manitoba; Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Lameness; Hierarchy; Analytic hierarchy process; Computer science; Engineering; Simulation; Operations research; Medicine; Political science","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002609883,0.00025639,0.0003709281,0.0001012287,0.0003814108,0.0001565248,0.0004032328,0.0001368493,0.000005018997],"category_scores_gemma":[0.00001634414,0.0002052347,0.00004245984,0.0004269335,0.00008991394,0.0001910715,0.0008461467,0.0005159299,5.20888e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001716341,"about_ca_system_score_gemma":0.0000880826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009460976,"about_ca_topic_score_gemma":0.00004863744,"domain_scores_codex":[0.9984002,0.00009566845,0.0003059831,0.000578343,0.0001382155,0.0004816029],"domain_scores_gemma":[0.9993727,0.0001085002,0.00009743876,0.0003195762,0.00005819589,0.00004354803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005854361,0.0005354791,0.5096714,0.0008750204,0.0009076609,0.0006640023,0.003770497,0.0008419845,0.1428656,0.2217462,0.008161269,0.1093754],"study_design_scores_gemma":[0.009726114,0.001673055,0.7927601,0.003913139,0.0009426206,0.001880248,0.01711889,0.05966712,0.001245607,0.007390959,0.09920249,0.004479614],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874004,0.00336902,0.005249145,0.0002742333,0.0003436811,0.000220226,0.000027396,0.00009630893,0.003019589],"genre_scores_gemma":[0.9941953,0.000249595,0.005153571,0.0001407524,0.00008383126,0.000008781044,0.00009015523,0.00001273874,0.00006526336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2830887,"threshold_uncertainty_score":0.8369228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06031699989306258,"score_gpt":0.3630806054345169,"score_spread":0.3027636055414543,"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."}}