{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002074609,0.000513254,0.0003021176,0.0004123792,0.0001023893,0.0002059612,0.0003432025,0.0002172994,0.001039871],"category_scores_gemma":[0.0001386671,0.0002416956,0.0005029507,0.0002367874,0.0001504134,0.000121845,0.000348243,0.000629616,0.0003025142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002768467,"about_ca_system_score_gemma":0.0002120135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001122899,"about_ca_topic_score_gemma":0.001802805,"domain_scores_codex":[0.9998158,0.00002173512,0.00002706603,0.00005077063,0.00004772385,0.00003700732],"domain_scores_gemma":[0.9997801,0.00003346733,0.0000813729,0.00002351725,0.00002026444,0.00006122323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004236982,0.00002817162,0.0002475588,0.00001414218,0.000008448925,0.00004606627,0.00001581866,0.0000768576,0.9985557,0.00002762294,0.00001264152,0.0009245532],"study_design_scores_gemma":[0.0002079377,0.0016285,0.08719381,0.00002458855,0.000356415,0.0009414512,0.0001275957,0.007248444,0.8971437,0.0001055884,0.004988396,0.0000336364],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915673,0.0001049321,0.007002453,0.00003322088,0.00001262394,0.00009229554,0.0004997042,0.0001972411,0.0004902471],"genre_scores_gemma":[0.9844909,0.0001564188,0.009237147,0.00005855252,0.00001061544,0.00008784429,0.002259664,0.0001007377,0.003598057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001122899,"threshold_uncertainty_score":0.003478706,"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."}}