{"id":"W4408240612","doi":"10.5376/mpb.2025.16.0004","title":"Marker-Assisted Selection (MAS) in Soybean Breeding","year":2025,"lang":"en","type":"article","venue":"Molecular Plant Breeding","topic":"Soybean genetics and cultivation","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Biology; Marker-assisted selection; Selection (genetic algorithm); Biotechnology; Genetic marker; Genomic selection; Microsatellite; Plant breeding; Genetics; Computational biology; Agronomy; Gene; Genotype; Allele; Computer science; Single-nucleotide polymorphism; Artificial intelligence","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":[0.0002328101,0.0001207685,0.0001251607,0.00005110883,0.0001357391,0.00008353945,0.0001392871,0.00009932638,0.0000449841],"category_scores_gemma":[0.00005091318,0.00006130814,0.0000573038,0.0007163406,0.00001435884,0.00005702917,0.00005119401,0.0001244123,0.000006099455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000056563,"about_ca_system_score_gemma":0.000007596205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002236292,"about_ca_topic_score_gemma":0.0003847918,"domain_scores_codex":[0.9990747,0.0000404443,0.0002048943,0.0002720653,0.0001573808,0.0002505172],"domain_scores_gemma":[0.9997881,0.00005380493,0.00005364368,0.00002955605,0.00003871709,0.00003614773],"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":[0.00001714777,0.00003065342,0.04060634,0.000005831882,0.00001078661,0.000008767054,0.00002193127,0.00001317697,0.9296877,0.0009827283,0.0004414922,0.02817343],"study_design_scores_gemma":[0.0004016153,0.0001633279,0.8610294,0.0002136107,0.00002186922,0.00002461206,0.0004048424,0.005544505,0.1261217,0.0009634351,0.004774035,0.0003371288],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929746,0.00007268195,0.0001549603,0.0006956634,0.0001550313,0.0001454778,0.000009337174,0.00006262951,0.005729577],"genre_scores_gemma":[0.9992459,0.000009078294,0.000157755,0.0002520433,0.00006720584,0.00001291428,0.00007589391,0.000001091565,0.0001781377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.820423,"threshold_uncertainty_score":0.2500074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01818632750949328,"score_gpt":0.2135650727501759,"score_spread":0.1953787452406826,"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."}}