{"id":"W2796096269","doi":"10.1139/cjas-2017-0192","title":"Genomic retained heterosis effects on fertility and lifetime productivity in beef heifers","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Animal Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Congress of Aboriginal Peoples; Alberta Livestock and Meat Agency; Alberta Crop Industry Development Fund; Agriculture Food and Rural Development","funders":"Alberta Agriculture and Forestry; Genome Alberta; Agriculture and Agri-Food Canada; Alberta Livestock and Meat Agency; Alberta Innovates; Alberta Innovates Bio Solutions","keywords":"Heterosis; Breed; Crossbreed; Biology; Animal science; Herd; Sire; Beef cattle; Loss of heterozygosity; Fertility; Productivity; Inbreeding; Biotechnology; Veterinary medicine; Genetics; Population; Agronomy; Allele; Gene; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0007114558,0.0001292287,0.0001596038,0.0003652867,0.0001754101,0.0003027534,0.0001273112,0.0001248245,0.0003114286],"category_scores_gemma":[0.001107818,0.0001265756,0.0001643694,0.0002371428,0.000299286,0.0001884872,0.0002259338,0.000196565,0.0000393636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003843421,"about_ca_system_score_gemma":0.000164652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005345988,"about_ca_topic_score_gemma":0.01332611,"domain_scores_codex":[0.999688,0.0001006544,0.00001690974,0.0000793777,0.00007718347,0.00003784881],"domain_scores_gemma":[0.9991068,0.0002881803,0.000318047,0.0000751215,0.00009043149,0.0001214985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005510119,0.00009658356,0.9450364,0.00001633096,0.0001909313,0.0001546852,0.0002812578,0.0006951047,0.04124213,0.00005733618,0.00003580288,0.01164245],"study_design_scores_gemma":[6.026887e-7,0.00009601757,0.9992489,6.791997e-7,0.000007110059,0.00003389688,0.00002256598,0.0001670029,0.0003971173,0.000007088677,0.00001768935,0.00000127239],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997792,0.00006449348,0.0000746674,0.000002388669,3.482239e-7,5.086501e-7,0.00002362154,0.000001481181,0.00005329592],"genre_scores_gemma":[0.9998277,0.00001857878,0.0000468176,0.000003350736,7.467687e-7,6.419003e-7,0.00003907845,9.910907e-7,0.00006194072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005345988,"threshold_uncertainty_score":0.01062977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00951917134768157,"score_gpt":0.2274038722735734,"score_spread":0.2178847009258918,"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."}}