{"id":"W4406038244","doi":"10.5376/lgg.2024.15.0026","title":"Integrating GWAS and Genomic Selection to Enhance Soybean Breeding","year":2024,"lang":"en","type":"article","venue":"Legume Genomics and Genetics","topic":"Soybean genetics and cultivation","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Genomic selection; Genome-wide association study; Selection (genetic algorithm); Computational biology; Biology; Biotechnology; Computer science; Genetics; Artificial intelligence; Single-nucleotide polymorphism; Gene; Genotype","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001404399,0.0001248485,0.00009742156,0.00001557121,0.000183089,0.0002885719,0.0000725361,0.00006817553,0.00002499156],"category_scores_gemma":[0.00001007592,0.00006302811,0.0000262298,0.000173533,0.00002472921,0.00003372763,0.00008149599,0.00009224434,0.0000132312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002862196,"about_ca_system_score_gemma":0.000008334355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004656941,"about_ca_topic_score_gemma":0.0002553527,"domain_scores_codex":[0.999228,0.00001479434,0.0001616602,0.0003312373,0.0000711679,0.0001931322],"domain_scores_gemma":[0.9997637,0.00004688575,0.00002572975,0.00002979834,0.00003238355,0.0001015437],"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":[0.000003120652,0.000004007493,0.001966981,0.000007536541,0.000007979571,5.165036e-7,0.0003586437,0.00001991153,0.7482274,0.0003512654,0.0001226705,0.24893],"study_design_scores_gemma":[0.0002393108,0.002148518,0.3701971,0.0002286211,0.0001284756,0.0001246701,0.003376978,0.06203987,0.2068671,0.01091271,0.3419705,0.001766128],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959162,0.002370935,0.0002712006,0.0008126796,0.000164211,0.0001550045,0.00001460788,0.00004287571,0.0002522675],"genre_scores_gemma":[0.9961997,0.0005147047,0.0022832,0.0002509549,0.0004657079,0.000007610704,0.0000127683,0.000002913722,0.0002624561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5413603,"threshold_uncertainty_score":0.2782705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01165701990076308,"score_gpt":0.2324561749512058,"score_spread":0.2207991550504427,"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."}}