{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004246935,0.00007513843,0.0001024252,0.00001164778,0.0008775952,0.0001920697,0.0007540635,0.00003273895,0.00006044324],"category_scores_gemma":[0.0001919245,0.00003015709,0.0000447135,0.0002269012,0.0008276009,0.0002324342,0.0001301857,0.00005324246,0.00001295316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001249735,"about_ca_system_score_gemma":0.00002065171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001834444,"about_ca_topic_score_gemma":0.00006302597,"domain_scores_codex":[0.9990768,0.00001441515,0.0001449446,0.0002642073,0.0002854463,0.0002141367],"domain_scores_gemma":[0.9994075,0.00003443987,0.000190036,0.0001255252,0.0001694615,0.00007306141],"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":[0.000004472149,0.00002207527,0.002783706,0.000002288779,0.000001341283,6.831913e-7,0.0001683963,0.00000188647,0.9786225,0.001416223,0.00005316462,0.01692325],"study_design_scores_gemma":[0.0000709549,0.0001108198,0.676177,0.00001819801,0.000002496914,0.000001760468,0.0003882814,0.0003034433,0.3201287,0.001975303,0.0007078305,0.0001152286],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959381,0.00005817145,0.00004833485,0.000520382,0.0001422338,0.00008908943,0.0000224583,0.00001451986,0.003166707],"genre_scores_gemma":[0.9986671,0.000007285941,0.001098194,0.00006193092,0.00005576523,0.000002371767,0.000003084728,4.007273e-7,0.0001038998],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6733933,"threshold_uncertainty_score":0.6749842,"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."}}