{"id":"W2108460578","doi":"10.1017/s001667230900007x","title":"Breeding without breeding","year":2009,"lang":"en","type":"article","venue":"Genetics Research","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":135,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Národní Agentura pro Zemědělský Výzkum; Natural Sciences and Engineering Research Council of Canada; Grantová Agentura České Republiky","keywords":"Selection (genetic algorithm); Tree breeding; Biology; Progeny testing; Mating design; Genetic gain; Mating; Biotechnology; Plant breeding; Hybrid; Evolutionary biology; Genetic variation; Genetics; Ecology; Computer science; Agronomy; Heterosis; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007061551,0.0001647431,0.0001362211,0.0001200593,0.0002592645,0.00007740139,0.000521075,0.0001962643,0.00005306975],"category_scores_gemma":[0.0001242428,0.0001645616,0.00006787715,0.0002407647,0.0001782831,0.0000025254,0.0001879553,0.0003037623,0.00007102078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001945148,"about_ca_system_score_gemma":0.0001260455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006848643,"about_ca_topic_score_gemma":0.000007410133,"domain_scores_codex":[0.9979631,0.0001099192,0.0002145125,0.0004912729,0.0005361667,0.000685018],"domain_scores_gemma":[0.9989453,0.00002223234,0.00003416764,0.0005091076,0.0002767968,0.0002124104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001084389,0.0001265001,0.009567375,0.00001646141,0.00003959692,0.000002068611,0.0002438805,0.0007610337,0.9036883,0.005682588,0.01265416,0.06710962],"study_design_scores_gemma":[0.0023167,0.01045253,0.3570443,0.00008970971,0.00003864395,0.0001225723,0.0007645586,0.0005703541,0.395334,0.03533001,0.1966611,0.001275584],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9542434,0.002093093,0.007162167,0.0004973352,0.0002160427,0.0003103588,0.000007406813,0.00002559614,0.03544463],"genre_scores_gemma":[0.970275,0.0001914848,0.02514427,0.0002002992,0.0009636012,0.00001204034,0.00002274337,0.00002644599,0.003164046],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5083543,"threshold_uncertainty_score":0.6710629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07351467109268973,"score_gpt":0.3736860834577987,"score_spread":0.300171412365109,"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."}}