{"id":"W2740226261","doi":"10.2135/cropsci2017.02.0106","title":"Quantitative Trait Locus Mapping of Soybean Maturity Gene <i>E6</i>","year":2017,"lang":"en","type":"article","venue":"Crop Science","topic":"Soybean genetics and cultivation","field":"Agricultural and Biological Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"National Key Research and Development Program of China; Agricultural Research Service; Chinese Academy of Sciences; University of Illinois at Urbana-Champaign; National Natural Science Foundation of China; U.S. Department of Agriculture","keywords":"Biology; Locus (genetics); Quantitative trait locus; Cultivar; Trait; Genetics; Gene; Gene mapping; Genetic marker; Marker-assisted selection; Botany","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":[0.0001185683,0.0002016213,0.00009989829,0.0002604212,0.0001154201,0.0001068531,0.0001569831,0.0001277851,0.000848772],"category_scores_gemma":[0.0001145446,0.0001210839,0.000192259,0.0001576058,0.0001389259,0.0000666741,0.0001476529,0.000284807,0.0002511571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002367736,"about_ca_system_score_gemma":0.0001126341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001190531,"about_ca_topic_score_gemma":0.002362101,"domain_scores_codex":[0.9999219,0.00001071534,0.00000568302,0.00003234176,0.00001811093,0.00001115246],"domain_scores_gemma":[0.9999031,0.00001763661,0.00003388399,0.000005138581,0.00001048753,0.00002975198],"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":[0.00007658638,0.00001780764,0.0009657791,0.00001156124,0.000003125199,0.00003288211,0.00002264226,0.00004461173,0.9973291,0.00007004542,0.00002030944,0.001405528],"study_design_scores_gemma":[0.0001597718,0.0005970917,0.4097223,0.00003195768,0.00007920239,0.001596517,0.0001496275,0.00520493,0.5738122,0.0003857975,0.008224914,0.00003563197],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939758,0.0001309427,0.003921316,0.00004382008,0.00001050538,0.00002148496,0.0008574295,0.00004647815,0.0009922954],"genre_scores_gemma":[0.9897872,0.0001217882,0.00487599,0.00004401235,0.000006068473,0.00001653993,0.003151882,0.00003691833,0.001959549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001190531,"threshold_uncertainty_score":0.002839446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05471723904449377,"score_gpt":0.2735600635167906,"score_spread":0.2188428244722969,"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."}}