{"id":"W4417015490","doi":"10.5376/lgg.2025.16.0010","title":"Genomic Prediction of Yield and Protein Traits in Soybean Using Machine Learning Models","year":2025,"lang":"","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":"Support vector machine; Random forest; Feature selection; Artificial neural network; Lasso (programming language); Selection (genetic algorithm); Principal component analysis; Stability (learning theory); Generalization","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":[0.0004262867,0.0004884949,0.0003002632,0.0002917709,0.00012443,0.0002685271,0.0001977314,0.0002331683,0.000185924],"category_scores_gemma":[0.000751639,0.0001414865,0.0003816758,0.0002583264,0.0001137381,0.0002379484,0.0001637443,0.0002718522,0.00007277074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004168512,"about_ca_system_score_gemma":0.0003687376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009940257,"about_ca_topic_score_gemma":0.009132992,"domain_scores_codex":[0.9999137,0.00002541034,0.00000434189,0.00003460328,0.00001134339,0.00001072885],"domain_scores_gemma":[0.9997965,0.0001266151,0.00003283191,0.000009177608,0.00002670855,0.000008348979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009558497,0.0000752307,0.0187937,0.00002790835,0.00004878423,0.00007847615,0.00001964636,0.9330516,0.009173107,0.0004534978,0.0002668954,0.03791559],"study_design_scores_gemma":[0.000001425714,0.000008510352,0.001880151,6.79345e-7,0.000003222654,0.000002947425,0.000001899755,0.9975411,0.0003831602,0.0001525922,0.00002261472,0.00000173008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8284224,0.0004527328,0.1697383,0.0001345057,0.0000125467,0.00001195607,0.0002628541,0.0003958798,0.0005688292],"genre_scores_gemma":[0.9813774,0.0001106076,0.01778174,0.00001448814,0.000003656269,0.00001112627,0.0003645747,0.00001151781,0.0003248485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009940257,"threshold_uncertainty_score":0.01976484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0354016298034752,"score_gpt":0.2147126156980337,"score_spread":0.1793109858945585,"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."}}