{"id":"W3129910348","doi":"10.1002/tpg2.20088","title":"Modeling first order additive × additive epistasis improves accuracy of genomic prediction for sclerotinia stem rot resistance in canola","year":2021,"lang":"en","type":"article","venue":"The Plant Genome","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Grains Research and Development Corporation","keywords":"Sclerotinia sclerotiorum; Canola; Sclerotinia; Epistasis; Leptosphaeria maculans; Biology; Quantitative trait locus; Genetic architecture; Best linear unbiased prediction; Stem rot; Plant breeding; Quantitative genetics; Genetics; Biotechnology; Agronomy; Selection (genetic algorithm); Genetic variation; Botany; Machine learning; Gene; Computer science","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":[0.003020197,0.0008973629,0.0005212014,0.000504449,0.0002184026,0.0006158219,0.0004618874,0.0003946286,0.0005067866],"category_scores_gemma":[0.004466348,0.000240236,0.0008083531,0.0002883806,0.000164772,0.0005088079,0.0005803137,0.0006980621,0.0001841565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003734126,"about_ca_system_score_gemma":0.0005136445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008755426,"about_ca_topic_score_gemma":0.01744269,"domain_scores_codex":[0.9987896,0.0007655476,0.00004048735,0.0002605176,0.00008520918,0.00005861856],"domain_scores_gemma":[0.9968587,0.002508815,0.0002086829,0.0001834539,0.0001551069,0.00008524729],"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":[0.0007746473,0.0003558046,0.1289271,0.000132521,0.001406406,0.000128481,0.0001940299,0.6920542,0.04999151,0.001214648,0.0005639171,0.1242567],"study_design_scores_gemma":[0.00001336297,0.0001812439,0.02797714,0.000006376632,0.0001250681,0.00002846026,0.00001963847,0.9671348,0.0033698,0.0008571349,0.0002607041,0.0000263596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8970683,0.0003773478,0.1007053,0.0001541142,0.00001067964,0.00001237685,0.0002327945,0.0008603928,0.0005788028],"genre_scores_gemma":[0.9774843,0.00008683524,0.02167798,0.0000389191,0.000004347297,0.00001363298,0.0003065793,0.00006965036,0.0003177322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008755426,"threshold_uncertainty_score":0.01740891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04161749030661192,"score_gpt":0.2005426743560403,"score_spread":0.1589251840494284,"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."}}