{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002181421,0.0002833574,0.0003006063,0.0009361954,0.0002880064,0.0004174362,0.0003512698,0.0003610456,0.001957588],"category_scores_gemma":[0.001897924,0.0001380382,0.0006990996,0.0009950238,0.0002441837,0.0001082424,0.0003186627,0.0004222009,0.0001431126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001419611,"about_ca_system_score_gemma":0.0002120879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002323264,"about_ca_topic_score_gemma":0.003478986,"domain_scores_codex":[0.9989346,0.0004361939,0.00007450216,0.0003301774,0.0001357195,0.00008873528],"domain_scores_gemma":[0.9980193,0.001114471,0.0004028674,0.0001827216,0.0001491101,0.0001315783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008964767,0.0001025874,0.9673256,0.00004780308,0.001594491,0.0002271448,0.0001290232,0.0004736402,0.02103864,0.0000659724,0.0001033031,0.007995282],"study_design_scores_gemma":[0.00001766471,0.0001495143,0.9976865,0.000004129722,0.0003700267,0.0002017997,0.00003655444,0.000590619,0.0007430112,0.00003884681,0.0001582422,0.000003017392],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997812,0.0003567528,0.00136024,0.00002462615,0.000005571704,0.000005511142,0.0002471983,0.00001399647,0.0001740145],"genre_scores_gemma":[0.9978264,0.00008697984,0.001314801,0.00002716068,0.00000712555,0.00001471975,0.0005422733,0.000007490836,0.0001731296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002323264,"threshold_uncertainty_score":0.0115366,"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."}}