{"id":"W1977692059","doi":"10.1046/j.1439-0388.2000.00252.x","title":"Mate selection strategies to exploit across‐ and within‐breed dominance variation","year":2000,"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":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Breed; Biology; Selection (genetic algorithm); Truncation selection; Heterosis; Dominance (genetics); Statistics; Genetic variation; Mathematics; Ecology; Genetics; Agronomy; Computer science","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":[0.001634168,0.0002748257,0.0004678116,0.0002298873,0.0002398293,0.0005182128,0.0009460313,0.0004113804,0.002161098],"category_scores_gemma":[0.001610254,0.0001984221,0.0005258449,0.0001774938,0.0002414677,0.0005176537,0.0007321126,0.0006275908,0.0004227011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003007344,"about_ca_system_score_gemma":0.0002947918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004224076,"about_ca_topic_score_gemma":0.0009280811,"domain_scores_codex":[0.9995866,0.0002030734,0.00001471146,0.00007858274,0.00008683845,0.00003020291],"domain_scores_gemma":[0.9992101,0.0003454358,0.0001219347,0.0001496417,0.0001021931,0.00007057102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000616754,0.000465531,0.0511299,0.0001636687,0.0005965898,0.0004498493,0.0003887708,0.5696234,0.1597605,0.02720131,0.001650385,0.1879533],"study_design_scores_gemma":[0.00002779292,0.0003271761,0.01032428,0.00001241523,0.00006575608,0.0002269828,0.00003498769,0.9722357,0.008121272,0.007060531,0.001533924,0.0000292225],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6369046,0.0003355801,0.3572673,0.0001401696,0.00003965948,0.00007011377,0.00009086195,0.0002096029,0.004942236],"genre_scores_gemma":[0.9515743,0.00006998451,0.04645752,0.00007029521,0.00001034481,0.00006135686,0.00008671952,0.0000314314,0.001638051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002161098,"threshold_uncertainty_score":0.008642375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01261017723874753,"score_gpt":0.261707342652753,"score_spread":0.2490971654140055,"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."}}