{"id":"W3111544259","doi":"10.1093/jas/skaa379","title":"Genetic parameters and purebred–crossbred genetic correlations for growth, meat quality, and carcass traits in pigs","year":2020,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Research Council Canada; National Institute of Food and Agriculture; Government of Canada; U.S. Department of Agriculture","keywords":"Purebred; Heritability; Sire; Crossbreed; Selection (genetic algorithm); Biology; Best linear unbiased prediction; Genetic correlation; Restricted maximum likelihood; Population; Statistics; Biotechnology; Genetic variation; Animal science; Mathematics; Genetics; Maximum likelihood; Demography; Gene","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":[0.001908423,0.0004410112,0.0002619424,0.001381476,0.0002009177,0.0003231927,0.0002405172,0.0003148339,0.0005759213],"category_scores_gemma":[0.002008758,0.0002459119,0.0003650535,0.0009219575,0.0003358177,0.0002640344,0.0003182962,0.0002902638,0.0001756039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001571197,"about_ca_system_score_gemma":0.0002104988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001640882,"about_ca_topic_score_gemma":0.002584079,"domain_scores_codex":[0.9991047,0.0004132332,0.00006095928,0.0002370587,0.0001444538,0.00003960708],"domain_scores_gemma":[0.9977585,0.001169225,0.0004739729,0.000351764,0.0001554864,0.00009102263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000476481,0.0002587015,0.8910729,0.00005757226,0.0009439401,0.0004273278,0.0005183792,0.003181519,0.0822121,0.0003107265,0.00009219467,0.02044819],"study_design_scores_gemma":[0.000005704502,0.00009042358,0.9970976,0.000003222567,0.00005349716,0.0001504387,0.00003462525,0.001560529,0.0008610515,0.0000708364,0.00006552103,0.000006630762],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998604,0.00005854315,0.001132711,0.000003218808,0.000001215205,0.000003624059,0.00007920661,0.000006334531,0.0001111705],"genre_scores_gemma":[0.9978476,0.00005524059,0.001443304,0.000007714418,0.000004568754,0.00001359303,0.0004254012,0.00001145142,0.0001911169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001908423,"threshold_uncertainty_score":0.01009279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03970456254500909,"score_gpt":0.2965790774827403,"score_spread":0.2568745149377312,"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."}}