{"id":"W3133644862","doi":"10.3389/fgene.2020.592769","title":"Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project","year":2021,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Agricultural Research Service; Iowa State University; University of Wisconsin-Madison; U.S. Department of Agriculture","keywords":"Genomic selection; Gene–environment interaction; Environmental data; Predictive modelling; Covariate; Biology; Computer science; Set (abstract data type); Best linear unbiased prediction; Genotype; Selection (genetic algorithm); Machine learning; Genetics; Ecology","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":[0.0003411284,0.000140029,0.0001930094,0.00005011138,0.00006817068,0.00002051783,0.0002380057,0.0001631092,0.000003378487],"category_scores_gemma":[0.0001324496,0.0001267813,0.00007450956,0.0001386,0.00005590845,0.000007520415,0.0001489957,0.00009608302,6.973502e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001931444,"about_ca_system_score_gemma":0.0002000303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001781676,"about_ca_topic_score_gemma":0.00005241409,"domain_scores_codex":[0.9988455,0.00006134525,0.000486389,0.0002676843,0.0001231972,0.0002159281],"domain_scores_gemma":[0.999138,0.00002939766,0.0001109533,0.0005075932,0.0001552273,0.00005882526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002096535,0.0006362816,0.1070069,0.001382814,0.0005321513,5.548073e-7,0.02796482,0.52239,0.1643507,0.001877846,0.02733415,0.1444273],"study_design_scores_gemma":[0.004069865,0.005361362,0.2983045,0.0001820258,0.000338275,0.00001728901,0.01344723,0.2311736,0.3564182,0.07714889,0.01202109,0.001517654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4682965,0.0003365842,0.5295076,0.00008662978,0.0005905272,0.0008315434,0.0001029957,0.000004661101,0.0002429526],"genre_scores_gemma":[0.7704286,0.00002608974,0.228958,0.0002957461,0.00006224567,0.0001073812,0.00006201857,0.00000988123,0.00005003822],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.302132,"threshold_uncertainty_score":0.5169989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01749052804151231,"score_gpt":0.2357883243430892,"score_spread":0.2182977963015769,"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."}}