{"id":"W2070492168","doi":"10.1186/1471-2156-14-80","title":"Genome-wide association analyses for carcass quality in crossbred beef cattle","year":2013,"lang":"en","type":"article","venue":"BMC Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"L'Alliance Boviteq; University of Guelph; University of Alberta","funders":"Genome Alberta; Beef Cattle Research Council; Mitacs; Agriculture and Agri-Food Canada; University of Guelph; Ministry of Agriculture, Food and Rural Affairs; Alberta Livestock and Meat Agency; Ontario Ministry of Agriculture, Food and Rural Affairs; Alberta Beef Producers","keywords":"Marbled meat; Biology; Single-nucleotide polymorphism; SNP; Crossbreed; Beef cattle; Tenderness; Meat tenderness; Genome-wide association study; Quantitative trait locus; Genetics; Genetic association; Animal science; Genotype; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002545956,0.0001695528,0.0001999042,0.00004315203,0.0000694905,0.00004636938,0.0002267099,0.0002325252,0.00003967996],"category_scores_gemma":[0.0005375133,0.000175766,0.0001184126,0.00009715043,0.00004420628,0.000002995117,0.00007614445,0.00007027352,0.00002780322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005211608,"about_ca_system_score_gemma":0.0001573292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001285901,"about_ca_topic_score_gemma":0.0005883468,"domain_scores_codex":[0.9986326,0.0001051004,0.000371497,0.0003672169,0.0001555303,0.0003680902],"domain_scores_gemma":[0.9990346,0.0001280479,0.0001536506,0.0003711674,0.0002227026,0.00008978062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004124724,0.000124902,0.8948354,0.00006311201,0.00007054741,5.790559e-8,0.000197917,0.007257509,0.09401403,0.0001049305,0.002632993,0.0006573694],"study_design_scores_gemma":[0.0006986194,0.0001505387,0.9686953,0.000003078986,0.00002451089,3.709487e-7,0.0001448214,0.00004985839,0.02352007,0.002559099,0.003920826,0.00023284],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9751204,0.001097094,0.02228191,0.000105221,0.0002134094,0.0005580815,0.00005630592,0.00001036747,0.0005571981],"genre_scores_gemma":[0.9451272,0.00003600449,0.05163061,0.0002481503,0.0002954086,0.0001533824,0.0001867861,0.00002673829,0.002295736],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07385998,"threshold_uncertainty_score":0.7167531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04901790788107478,"score_gpt":0.33363776983252,"score_spread":0.2846198619514452,"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."}}