{"id":"W4400315104","doi":"10.3168/jds.2024-25034","title":"Predicting subacute ruminal acidosis from milk mid-infrared estimated fatty acids and machine learning on Canadian commercial dairy herds","year":2024,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Ruminant Nutrition and Digestive Physiology","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ste. Anne's Hospital; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Novalait; Canadian Dairy Commission","keywords":"Dairy cattle; Herd; Mathematics; Dairy industry; Animal science; Machine learning; Food science; Chemistry; Computer science; Biology","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":[0.001584378,0.0006832799,0.0005063089,0.0005945893,0.0006308086,0.00075577,0.0009040418,0.0005690468,0.0004218074],"category_scores_gemma":[0.002107623,0.0002734879,0.0006269735,0.0006849405,0.0004026919,0.0002808023,0.0003994859,0.0003898517,0.0001301425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00445862,"about_ca_system_score_gemma":0.003276852,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7325123,"about_ca_topic_score_gemma":0.7572006,"domain_scores_codex":[0.9995719,0.00008739313,0.00001952181,0.0001604838,0.0000700685,0.00009061822],"domain_scores_gemma":[0.9992049,0.0003180946,0.0000877469,0.00006111349,0.0002583429,0.00006971171],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007599103,0.0002502791,0.8972161,0.00006479004,0.0003475821,0.0001755977,0.0002157105,0.06173293,0.004876706,0.0001183701,0.0005106364,0.03373135],"study_design_scores_gemma":[0.00002648121,0.0002231232,0.6698224,0.0000213314,0.0001160944,0.00006077713,0.0003918679,0.3270353,0.001546779,0.0001043506,0.0006231598,0.00002825483],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991032,0.00008309478,0.0004653331,0.00002695455,0.000001939303,0.000006365422,0.0002047771,0.00001300244,0.00009534234],"genre_scores_gemma":[0.9968454,0.0000946138,0.001577632,0.00001842094,0.000002533501,0.000007965294,0.00118654,0.00000504702,0.0002617621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2674877,"threshold_uncertainty_score":0.538126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0326744070042032,"score_gpt":0.261844561652029,"score_spread":0.2291701546478258,"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."}}