{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007088474,0.001081154,0.001087156,0.001993987,0.0003493608,0.001305648,0.0007172474,0.0006763493,0.002059333],"category_scores_gemma":[0.004973045,0.0004411501,0.0009304269,0.001902237,0.0003913006,0.0008752338,0.001728842,0.0008935572,0.0004996684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005397793,"about_ca_system_score_gemma":0.001247417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001970074,"about_ca_topic_score_gemma":0.005731961,"domain_scores_codex":[0.9973061,0.001622174,0.000134249,0.0004424366,0.0003434075,0.0001516747],"domain_scores_gemma":[0.9970986,0.001736888,0.0003166968,0.0003130275,0.0003452674,0.0001895899],"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":[0.00126225,0.0006157157,0.124143,0.001009885,0.001850723,0.001047664,0.000640841,0.02711651,0.2336415,0.01097424,0.002345477,0.5953523],"study_design_scores_gemma":[0.0009738877,0.004244766,0.5312323,0.0009674235,0.005337184,0.002279697,0.001441404,0.2125509,0.07478291,0.05342324,0.1121987,0.0005676044],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4772943,0.007969323,0.4888859,0.003993036,0.0004722372,0.0006044665,0.00269254,0.003828576,0.01425966],"genre_scores_gemma":[0.6317821,0.003685665,0.3581168,0.001978574,0.0002122645,0.0002128612,0.001751369,0.0003008418,0.001959502],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007088474,"threshold_uncertainty_score":0.03748786,"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."}}