{"id":"W1985051119","doi":"10.1007/bf03194611","title":"Adjustments for heterogeneous herd-year variances in a random regression model for genetic evaluations of Polish Black-and-White cattle","year":2006,"lang":"en","type":"article","venue":"Journal of Applied Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Herd; Statistics; Biology; Variance (accounting); Random effects model; Regression; Regression analysis; Bayesian probability; Variance components; Selection (genetic algorithm); Mathematics; Econometrics; Animal science; Accounting","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":[],"consensus_categories":[],"category_scores_codex":[0.0002279245,0.0001564501,0.0002813237,0.00007502827,0.00004428864,0.00001241523,0.0001746223,0.0001499882,0.000003245685],"category_scores_gemma":[0.00002530234,0.0001381217,0.0001173052,0.00005507459,0.00009416294,0.000002352408,0.00003764797,0.00006105861,2.433549e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001395286,"about_ca_system_score_gemma":0.0001749192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002333684,"about_ca_topic_score_gemma":0.00002001553,"domain_scores_codex":[0.9988264,0.00002343618,0.0005708351,0.0002016091,0.000170453,0.000207231],"domain_scores_gemma":[0.9991424,0.000038296,0.0003746378,0.0001796448,0.0002021016,0.00006289146],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001748266,0.0002661529,0.002379158,0.0001209367,0.0001051002,2.470667e-7,0.0003552617,0.908589,0.0800042,0.0009182626,0.001112871,0.004400538],"study_design_scores_gemma":[0.05869194,0.006811704,0.2257326,0.0003680112,0.001482013,0.0001153984,0.0008596067,0.1047432,0.3805665,0.2143894,0.004474876,0.001764833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8286119,0.001345105,0.1690129,0.00004403463,0.0001007107,0.0006077708,0.00006401937,0.00000168296,0.0002118917],"genre_scores_gemma":[0.7776372,0.0001723557,0.2217481,0.00003858588,0.0002330321,0.00003379939,0.00001966437,0.00002188817,0.00009534619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8038458,"threshold_uncertainty_score":0.5632442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01455904989609987,"score_gpt":0.2770423615266144,"score_spread":0.2624833116305146,"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."}}