{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002451345,0.0001232741,0.0001883386,0.00001409812,0.0002090183,0.0000364838,0.0002016837,0.00006547214,0.00007071582],"category_scores_gemma":[0.00006531487,0.00005313117,0.00005947943,0.0001913245,0.00002719381,0.00004929666,0.00006483796,0.00009853621,0.000003778727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003952879,"about_ca_system_score_gemma":0.00003331219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003693637,"about_ca_topic_score_gemma":0.0111954,"domain_scores_codex":[0.9990241,0.00004973044,0.000283322,0.0002621825,0.0001231525,0.0002575048],"domain_scores_gemma":[0.9990468,0.0005941256,0.000120005,0.00006475416,0.0001321815,0.00004212137],"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":[0.0003051664,0.00007447904,0.0005249451,0.00005042056,0.00007296749,0.000008101529,0.001623364,0.003032134,0.9840779,0.0004559071,0.0002963288,0.009478269],"study_design_scores_gemma":[0.002539098,0.001089675,0.6929812,0.001003562,0.0002865906,0.0000867654,0.01736902,0.1157816,0.06593814,0.004501319,0.09654121,0.001881764],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9843765,0.0006449539,0.00007028684,0.0005225293,0.00009365252,0.0004107738,0.01360083,0.00001416333,0.000266241],"genre_scores_gemma":[0.9977795,0.0006062309,0.0002088977,0.00005251072,0.0001792921,0.0000624207,0.0009465798,0.000001849272,0.0001626694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9181398,"threshold_uncertainty_score":0.6247296,"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."}}