{"id":"W2738012665","doi":"10.3168/jds.2017-12723","title":"Deviations in behavior and productivity data before diagnosis of health disorders in cows milked with an automated system","year":2017,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Guelph","funders":"Agriculture and Agri-Food Canada; Abbott Diabetes Care; Canadian Dairy Commission; Dairy Farmers of Canada; University of Guelph","keywords":"Metritis; Milking; Medicine; Ketosis; Animal science; Automatic milking; Rumination; Barn; Ice calving; Lactation; Endocrinology; Diabetes mellitus; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005727428,0.0001391611,0.0002614245,0.0004343766,0.0002435745,0.0003259503,0.0001518522,0.0002299908,0.0003929269],"category_scores_gemma":[0.00151887,0.0001250124,0.0002324777,0.0002716683,0.0002146803,0.0001548965,0.0002564439,0.000325446,0.0001002464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005419181,"about_ca_system_score_gemma":0.0003133098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005812383,"about_ca_topic_score_gemma":0.01009929,"domain_scores_codex":[0.9996172,0.0001016486,0.0000294387,0.00008058342,0.0001000199,0.0000711522],"domain_scores_gemma":[0.9988802,0.0002804513,0.0004578336,0.00007605502,0.0001651486,0.0001403095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006119008,0.0001348956,0.9867408,0.000006844975,0.00002505338,0.00004235048,0.0001541182,0.0001072225,0.008976111,0.000006191774,0.00002794096,0.003166573],"study_design_scores_gemma":[0.000002019282,0.0003439099,0.9987168,9.783167e-7,0.000005395094,0.00002337836,0.00004603774,0.0001406388,0.000677944,0.000002603831,0.00003844569,0.000001735366],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997695,0.00001907205,0.00006978749,0.00000371182,6.61022e-7,0.000002814762,0.00007179126,0.000001866689,0.00006083839],"genre_scores_gemma":[0.9991842,0.0000210384,0.0002739714,0.00001151647,0.00000257227,0.00001106681,0.0003273468,9.312218e-7,0.0001674088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005812383,"threshold_uncertainty_score":0.0115571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08680665795452465,"score_gpt":0.3976776642237502,"score_spread":0.3108710062692256,"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."}}