{"id":"W2276382221","doi":"10.1111/asj.12570","title":"Estimation of variance and genomic prediction using genotypes imputed from low‐density marker subsets for carcass traits in Japanese black cattle","year":2015,"lang":"en","type":"article","venue":"Animal Science Journal","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics","keywords":"Estimation; Biology; Genotype; Genomic selection; Variance (accounting); Statistics; Variance components; Genetics; Mathematics; Gene; Single-nucleotide polymorphism; Business; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004856817,0.00007671952,0.00009984555,0.00004414276,0.00008104966,0.00003194345,0.000136513,0.00006711891,0.000002644389],"category_scores_gemma":[0.00009803999,0.00007123416,0.00002270767,0.00009969805,0.00022732,0.00002050076,0.00004761929,0.00006422469,4.513057e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003615855,"about_ca_system_score_gemma":0.0002727199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009867555,"about_ca_topic_score_gemma":0.00003621575,"domain_scores_codex":[0.9992614,0.00002723536,0.0001914839,0.0002026566,0.0001430335,0.0001741226],"domain_scores_gemma":[0.9995602,0.00001098136,0.0001101575,0.00007357915,0.0001303603,0.0001147021],"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":[0.0002535012,0.00003697043,0.01337365,0.000008301098,0.00001038314,4.613039e-7,0.0009609532,0.01479731,0.9682387,0.00004580104,0.00005004787,0.002223979],"study_design_scores_gemma":[0.0005979963,0.0003397147,0.9457228,0.00001812343,0.00001673296,0.00006213834,0.0002056841,0.02410673,0.02796089,0.0008724357,0.000009379612,0.00008738192],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985265,0.0001852502,0.0142247,0.00002388375,0.0001235724,0.0001198965,0.0000258291,0.000001875606,0.0000300143],"genre_scores_gemma":[0.9622359,0.000006997439,0.03757986,0.00001855573,0.0001391202,0.000001101761,0.000006087228,0.000005307427,0.000007029827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9402778,"threshold_uncertainty_score":0.2904845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02028678472059536,"score_gpt":0.2637542938274979,"score_spread":0.2434675091069025,"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."}}