{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000230898,0.000139591,0.0001027978,0.00001437061,0.0002318264,0.00004597398,0.0003425338,0.00006234538,0.00002718376],"category_scores_gemma":[0.00003142008,0.0001124636,0.00004585716,0.00007201284,0.00004120696,0.000002561652,0.0002760657,0.00010286,0.00006040881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001807224,"about_ca_system_score_gemma":0.00004280895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001617359,"about_ca_topic_score_gemma":0.00001157644,"domain_scores_codex":[0.9989651,0.00003506724,0.000222321,0.0003219967,0.0001138439,0.0003416871],"domain_scores_gemma":[0.9995188,0.0000107815,0.00007255475,0.0002254852,0.00002956155,0.0001428006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001285763,0.000009339719,0.001787641,0.000009203532,0.00005206782,5.984655e-7,0.001365023,0.003399007,0.9910098,0.0005871603,0.0009381611,0.0007134478],"study_design_scores_gemma":[0.002077453,0.002931237,0.3960787,0.00004157249,0.0001763383,0.0002186175,0.002424331,0.0005525746,0.3078383,0.0005694377,0.2859415,0.001150056],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918831,0.0002333818,0.004746258,0.0005945431,0.000241285,0.0003247951,0.0003515328,0.00002657988,0.001598543],"genre_scores_gemma":[0.9944987,0.00001685552,0.002740065,0.001134742,0.001087426,0.00001970025,0.0002148411,0.00002274869,0.0002649158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6831715,"threshold_uncertainty_score":0.4586133,"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."}}