{"id":"W2898634952","doi":"10.3168/jds.2018-15159","title":"Genomic prediction of lactation curves for milk, fat, protein, and somatic cell score in Holstein cattle","year":2018,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Agriculture and Agri-Food Canada; Canadian Dairy Commission; Dairy Farmers of Canada","keywords":"Best linear unbiased prediction; Holstein Cattle; Context (archaeology); Statistics; Trait; Population; Genomic selection; Dairy cattle; Linear regression; Somatic cell count; Lactation; Regression; Biology; Regression analysis; Reliability (semiconductor); Mathematics; Selection (genetic algorithm); Animal science; Genetics; Single-nucleotide polymorphism; Genotype; Demography; Computer science; Ice calving","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":[0.002543274,0.0004497347,0.0003492947,0.0006370783,0.0001568353,0.0005282621,0.0004196799,0.0003487972,0.0004566044],"category_scores_gemma":[0.0036542,0.0002150523,0.0006225959,0.0004334525,0.0001921086,0.0002708022,0.0003891922,0.000413618,0.00018859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005219748,"about_ca_system_score_gemma":0.0003068261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004237765,"about_ca_topic_score_gemma":0.005329558,"domain_scores_codex":[0.9993402,0.0003812373,0.00001937017,0.0001687523,0.00004179646,0.00004857592],"domain_scores_gemma":[0.9981197,0.001310697,0.0002361847,0.0001466146,0.0001252245,0.00006169219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001506154,0.0002247297,0.5811059,0.0001085297,0.0008055305,0.0003021762,0.0004079994,0.2537451,0.03872728,0.0008207134,0.0004131059,0.1218327],"study_design_scores_gemma":[0.00001921923,0.0003181919,0.4493922,0.00002674848,0.000128843,0.0001250729,0.00007749367,0.5430163,0.005242805,0.001249105,0.0003605886,0.00004348303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9703499,0.0001545798,0.02900142,0.00002424383,0.000002672028,0.000006642447,0.0001916401,0.0001098986,0.0001590376],"genre_scores_gemma":[0.9827413,0.00006090572,0.0158437,0.00001833107,0.000003345488,0.00001469304,0.001049881,0.00003340533,0.0002344223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004237765,"threshold_uncertainty_score":0.01345026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01563950227402216,"score_gpt":0.2468726421059387,"score_spread":0.2312331398319166,"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."}}