{"id":"W4412121617","doi":"10.1111/pbr.70004","title":"Pyramiding of Genes/QTL for Resistance Against Three Rusts, High Grain Protein Content and Pre‐Harvest Sprouting Tolerance in Wheat ( <scp> <i>Triticum aestivum</i> </scp> L.) Using Marker‐Assisted Selection","year":2025,"lang":"en","type":"article","venue":"Plant Breeding","topic":"Wheat and Barley Genetics and Pathology","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Indian Agricultural Research Institute; Indian Council of Agricultural Research; Department of Biotechnology, Ministry of Science and Technology, India; Punjab Agricultural University; Indian National Science Academy","keywords":"Biology; Marker-assisted selection; Quantitative trait locus; Selection (genetic algorithm); Sprouting; Gene; Plant disease resistance; Wheat grain; Resistance (ecology); Poaceae; Genetics; Botany; Agronomy","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":[0.0005387334,0.000184985,0.0003303206,0.00005331208,0.0002690465,0.00006204934,0.0001314826,0.000148972,9.262914e-7],"category_scores_gemma":[0.000157455,0.0001025629,0.00005887113,0.0003882163,0.00007207495,0.00009555157,0.00006161174,0.000133892,1.1424e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004549346,"about_ca_system_score_gemma":0.0000189187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004853803,"about_ca_topic_score_gemma":0.004455589,"domain_scores_codex":[0.9986271,0.00005778085,0.0004084575,0.0003871343,0.0001227968,0.0003966907],"domain_scores_gemma":[0.999385,0.0002713063,0.0001548658,0.00004095673,0.00009826897,0.00004961489],"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":[0.00006822201,0.0000488601,0.0501265,0.0001591181,0.000009428737,0.000003676576,0.00004429552,0.00003245186,0.9416019,0.0007432888,0.00002920738,0.007133064],"study_design_scores_gemma":[0.001540129,0.0005612572,0.6119189,0.002662882,0.00007345792,0.00002800921,0.0005356997,0.01622298,0.3619646,0.001380821,0.002749027,0.0003621931],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967837,0.001255398,0.0004206164,0.000240695,0.0001849555,0.000723659,0.0001250763,0.00003238903,0.000233543],"genre_scores_gemma":[0.9975036,0.00006795349,0.001886617,0.00006345895,0.0001434671,0.00005396033,0.00004960304,0.000002530021,0.0002288567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5796373,"threshold_uncertainty_score":0.4182392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03944855403017344,"score_gpt":0.2262934467951198,"score_spread":0.1868448927649463,"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."}}