{"id":"W3019427358","doi":"10.1590/0001-3765202020180697","title":"Genetic parameters for performance and carcass traits in a paternal 1 lineage of broiler","year":2020,"lang":"pt","type":"article","venue":"Anais da Academia Brasileira de Ciências","topic":"Animal Nutrition and Physiology","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universidade Estadual Paulista; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Empresa Brasileira de Pesquisa Agropecuária; Fundação de Amparo à Pesquisa do Estado de São Paulo; University of Guelph","keywords":"Heritability; Biology; Genetic correlation; Restricted maximum likelihood; Selection (genetic algorithm); Broiler; Genetic gain; Weight gain; Animal science; Feed conversion ratio; Genetic variation; Statistics; Body weight; Genetics; Maximum likelihood; Mathematics; Gene; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001829514,0.00025779,0.0004903193,0.00003223801,0.00009653586,0.00003101647,0.0003308954,0.0006705367,0.0001091138],"category_scores_gemma":[0.0001313511,0.0001507769,0.0001491958,0.0003681543,0.0002117719,0.0001236047,0.0000885621,0.0006486294,0.000009455848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002869582,"about_ca_system_score_gemma":0.00003072958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000215372,"about_ca_topic_score_gemma":0.0000847854,"domain_scores_codex":[0.9981732,0.0001409656,0.0005393443,0.0004872784,0.0001577666,0.0005014215],"domain_scores_gemma":[0.9990858,0.0003111261,0.0002123992,0.00004206018,0.00005890471,0.0002897386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006367346,0.0001117538,0.8319387,0.0004106281,0.00003955597,0.000009893203,0.001922053,0.00007411723,0.1429245,0.0000769335,0.002925395,0.01892976],"study_design_scores_gemma":[0.0007417529,0.001609841,0.9878839,0.0001226293,0.00003176969,0.000008714826,0.0005242195,0.006262077,0.001808568,0.00003197606,0.0006992646,0.0002752891],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997086,0.0008422027,0.0000200369,0.001261604,0.00004603227,0.0004145775,0.0002716089,0.00001930527,0.00003857728],"genre_scores_gemma":[0.9946337,0.0006387412,0.0002358043,0.004069761,0.000317458,0.00003311115,0.00002624436,0.000005174924,0.00003997536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1559452,"threshold_uncertainty_score":0.6148502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06348801168031078,"score_gpt":0.2692252050519064,"score_spread":0.2057371933715956,"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."}}