{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00225524,0.0005913994,0.001010685,0.001243682,0.0009651975,0.001344369,0.00146159,0.0008392744,0.008099842],"category_scores_gemma":[0.001898792,0.0004510724,0.0006754439,0.001044818,0.001161595,0.001386356,0.001883495,0.001942969,0.006859326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003290661,"about_ca_system_score_gemma":0.000842121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000720344,"about_ca_topic_score_gemma":0.001547812,"domain_scores_codex":[0.9981176,0.0005200452,0.0001061879,0.0005073724,0.0006220334,0.00012681],"domain_scores_gemma":[0.9985078,0.0002691706,0.0001665708,0.0007725186,0.0001399847,0.000144038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003119956,0.0004309696,0.006076287,0.000782221,0.0001191264,0.0006041418,0.000533621,0.0009874282,0.2918302,0.02937639,0.01123461,0.657713],"study_design_scores_gemma":[0.0001889574,0.003132051,0.03710395,0.0004759068,0.0003159325,0.01333513,0.0003562612,0.0104757,0.1383935,0.02805798,0.7678969,0.0002676658],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05366207,0.003141483,0.9007435,0.001084396,0.001534684,0.001062786,0.001066461,0.003617782,0.03408678],"genre_scores_gemma":[0.1555446,0.003309408,0.7940136,0.002518764,0.0003661051,0.001065507,0.002523864,0.001327908,0.0393302],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008099842,"threshold_uncertainty_score":0.02709669,"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."}}