{"id":"W4366813892","doi":"10.1016/j.meatsci.2023.109200","title":"Estimation of genetic parameters for primal tissue component traits in commercial crossbred beef cattle","year":2023,"lang":"en","type":"article","venue":"Meat Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; University of Manitoba; Agriculture and Agri-Food Canada","funders":"","keywords":"Heritability; Trait; Crossbreed; Selection (genetic algorithm); Genetic correlation; Biology; Beef cattle; Best linear unbiased prediction; Lean tissue; Restricted maximum likelihood; Biotechnology; Animal science; Component (thermodynamics); Statistics; Genetic variation; Mathematics; Genetics; Adipose tissue; Maximum likelihood; Computer science; Gene","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":[0.0002288295,0.00008235475,0.0001081238,0.00007477401,0.00008503016,0.00001548482,0.0002805518,0.00005196568,0.000003472329],"category_scores_gemma":[0.000103146,0.00008195821,0.00002874706,0.0002818351,0.000389685,0.000004137231,0.0000681227,0.00003150554,0.000005179504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001055783,"about_ca_system_score_gemma":0.0001334633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002865067,"about_ca_topic_score_gemma":0.00003662788,"domain_scores_codex":[0.9991104,0.00001896123,0.0001899463,0.00027183,0.0001629468,0.000245904],"domain_scores_gemma":[0.9996666,0.00002450398,0.00005073523,0.0001598303,0.00004467274,0.00005366593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00008363026,0.00009372333,0.003285339,0.00005149515,0.000006757351,3.306433e-7,0.0007784778,0.1054352,0.8176646,0.001268543,0.0004075128,0.0709244],"study_design_scores_gemma":[0.0004015361,0.0002877205,0.6905605,0.000009900053,0.00000516508,0.00000221958,0.00004020777,0.001911908,0.3056922,0.0007991311,0.0001914703,0.00009812287],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9783129,0.00005539745,0.02091571,0.00009298071,0.0001590585,0.0003097177,0.00002597196,0.00000825211,0.0001199881],"genre_scores_gemma":[0.9368666,0.000003117749,0.06291958,0.00003908281,0.00002653159,0.00002949991,0.00002513504,0.000006988886,0.00008342692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6872751,"threshold_uncertainty_score":0.3342158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02372524441504877,"score_gpt":0.2960312893936724,"score_spread":0.2723060449786237,"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."}}