{"id":"W3011875918","doi":"10.1002/tpg2.20004","title":"Implementing within‐cross genomic prediction to reduce oat breeding costs","year":2020,"lang":"en","type":"article","venue":"The Plant Genome","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Biotechnology and Biological Sciences Research Council; Directorate for Biological Sciences","keywords":"Genotyping; Biology; Genomic selection; Population; Genotype; Genetics; Computational biology; Biotechnology; Gene; Single-nucleotide polymorphism","routes":{"ca_aff":true,"ca_fund":false,"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":[0.004248815,0.0008942406,0.00089034,0.0006680297,0.000475784,0.0009686024,0.001279331,0.0004611137,0.002790486],"category_scores_gemma":[0.005063104,0.0003940725,0.0005694578,0.000812227,0.000251612,0.001000668,0.001112806,0.001050422,0.0008138228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006665168,"about_ca_system_score_gemma":0.001182343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004116409,"about_ca_topic_score_gemma":0.008372448,"domain_scores_codex":[0.9985378,0.0006595057,0.00006582255,0.0003445202,0.0002716194,0.0001207642],"domain_scores_gemma":[0.9953238,0.002610425,0.0004709993,0.0009749577,0.0004292601,0.0001904754],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001574256,0.001430812,0.1275689,0.000285175,0.0004925947,0.0005886039,0.0006498816,0.1179848,0.2449385,0.004568284,0.002739301,0.4971789],"study_design_scores_gemma":[0.0003186549,0.003223092,0.3040574,0.000137194,0.0006106583,0.0005725623,0.0005952831,0.4894951,0.1642116,0.01231903,0.0242172,0.0002422382],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6030979,0.0002724758,0.3865925,0.0003446752,0.00006760032,0.0004991473,0.001484899,0.002965519,0.004675331],"genre_scores_gemma":[0.7080914,0.0001346554,0.2864766,0.0002178854,0.00002113191,0.0003307925,0.002571125,0.0003407802,0.001815666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004248815,"threshold_uncertainty_score":0.02247012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02525128264299375,"score_gpt":0.2493613875213436,"score_spread":0.2241101048783498,"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."}}