{"id":"W2181198270","doi":"10.3390/genes6041215","title":"Identification of Candidate Genes for Seed Glucosinolate Content Using Association Mapping in Brassica napus L.","year":2015,"lang":"en","type":"article","venue":"Genes","topic":"Genomics, phytochemicals, and oxidative stress","field":"Biochemistry, Genetics and Molecular Biology","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"China Scholarship Council; Earmarked Fund for Modern Agro-industry Technology Research System; National Natural Science Foundation of China; National Science Foundation","keywords":"Brassica; Glucosinolate; Rapeseed; Biology; Gene; Single-nucleotide polymorphism; Genetics; Candidate gene; Botany; Genotype","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":[0.0002887566,0.00009936918,0.0001566973,0.00005026913,0.00003183923,0.00001747786,0.0001080512,0.0001118885,6.596247e-7],"category_scores_gemma":[0.0001296588,0.0001058953,0.00006037306,0.00006236254,0.00002529066,0.000006070138,0.00004749836,0.00002822612,0.000001142318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007610583,"about_ca_system_score_gemma":0.00007663253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009385689,"about_ca_topic_score_gemma":0.00007924504,"domain_scores_codex":[0.9991506,0.00004113082,0.0003115409,0.0002288761,0.00008887166,0.0001789845],"domain_scores_gemma":[0.9992499,0.000009846042,0.0002762788,0.0001638743,0.0002547858,0.00004530422],"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":[0.00004656123,0.00002672676,0.05435969,0.00002347753,0.0000320796,1.511418e-7,0.0001078609,0.0002623463,0.9440952,0.00000786159,0.00006896545,0.000969096],"study_design_scores_gemma":[0.0007390105,0.0000333502,0.01254582,0.00001208188,0.00001677591,9.273134e-7,0.0002602341,0.0007938562,0.9833918,0.0001309307,0.001952684,0.0001225176],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913956,0.002060008,0.005878255,0.00006993589,0.0002394053,0.0002574793,0.00007238834,0.000004786939,0.00002212028],"genre_scores_gemma":[0.9981477,0.0001618235,0.001074181,0.00003504549,0.0001692447,0.00002512876,0.0001565047,0.00001377006,0.0002165813],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04181387,"threshold_uncertainty_score":0.4318285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05291270046642199,"score_gpt":0.2785204659767518,"score_spread":0.2256077655103298,"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."}}