{"id":"W3034938215","doi":"10.1111/jbg.12484","title":"The value of incorporating carcass trait phenotypes in terminal sire selection indexes to improve carcass weight and quality of heavy lambs","year":2020,"lang":"en","type":"article","venue":"Journal of Animal Breeding and Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Agriculture, Food and Rural Affairs; University of Guelph","funders":"Ontario Sheep Farmers; Ontario Agri-Food Innovation Alliance; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Sire; Selection (genetic algorithm); Biology; Carcass weight; Trait; Animal science; Biotechnology; Heritability; Profitability index; Livestock; Genetic correlation; Genetic variation; Body weight; Genetics; Ecology; Business","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.002206762,0.0007507192,0.0005100714,0.001064077,0.0003004327,0.0007554088,0.0004592842,0.0002811934,0.0005300784],"category_scores_gemma":[0.002535244,0.0001644731,0.0003980994,0.0008158416,0.0003988344,0.0002477326,0.0004100599,0.0005307323,0.0001118367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001163072,"about_ca_system_score_gemma":0.001564509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03954063,"about_ca_topic_score_gemma":0.1535766,"domain_scores_codex":[0.9983699,0.0004489745,0.0001149769,0.000234711,0.0007154975,0.0001159234],"domain_scores_gemma":[0.9977767,0.0005824057,0.0004649449,0.0001680364,0.000659791,0.0003481396],"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":[0.001673384,0.0006175286,0.375851,0.000157539,0.0005129051,0.0003138965,0.0003587169,0.007427388,0.4710735,0.0003667036,0.0002837674,0.1413637],"study_design_scores_gemma":[0.00003028479,0.001066792,0.9678258,0.00002581165,0.0002353801,0.0001944049,0.0001065209,0.008149649,0.02120086,0.00007044554,0.001040961,0.00005305068],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915241,0.0001889382,0.006782499,0.00004569811,0.000007401308,0.00005786586,0.0002410857,0.00005077052,0.001101638],"genre_scores_gemma":[0.9598744,0.0002387251,0.03747935,0.00005963178,0.00001058277,0.00003331573,0.0008265094,0.00004048656,0.001437045],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03954063,"threshold_uncertainty_score":0.07862091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02229791459244882,"score_gpt":0.2716233453645951,"score_spread":0.2493254307721463,"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."}}