{"id":"W2321046040","doi":"10.2135/cropsci2013.10.0665","title":"Genetic Improvement of U.S. Soybean in Maturity Groups II, III, and IV","year":2014,"lang":"en","type":"article","venue":"Crop Science","topic":"Soybean genetics and cultivation","field":"Agricultural and Biological Sciences","cited_by":241,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"United Soybean Board","keywords":"Cultivar; Biology; Yield (engineering); Crop; Linear regression; Gene–environment interaction; Genetic gain; Agronomy; Animal science; Horticulture; Genetic variation; Mathematics; Genotype; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003636379,0.00005844236,0.00007797758,0.00001439612,0.000169136,0.00003629375,0.0002042292,0.00002606955,0.00004275566],"category_scores_gemma":[0.00004097389,0.00002362827,0.00001454454,0.000366555,0.0002823747,0.00006413754,0.0001261077,0.00003824773,0.000001048791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001120458,"about_ca_system_score_gemma":0.000007111128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001647289,"about_ca_topic_score_gemma":0.0001736649,"domain_scores_codex":[0.9992635,0.000009221194,0.0001403782,0.0002283058,0.0001874488,0.0001711315],"domain_scores_gemma":[0.9997659,0.00002206061,0.00005599736,0.00005062917,0.00004863739,0.00005680638],"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":[0.000003008222,0.00002873934,0.01851005,0.000002044098,3.103162e-7,1.122208e-7,0.0001464179,0.000002492817,0.9085584,0.0003412877,0.00001670625,0.07239042],"study_design_scores_gemma":[0.0001132249,0.0002343664,0.951322,0.00000958643,0.000001250084,7.449046e-7,0.0001295202,0.0009881736,0.04483925,0.001937677,0.0003467131,0.00007747747],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990667,0.0000495283,0.00001107618,0.0001801089,0.00005585287,0.00009510464,0.000001690058,0.000007448596,0.0005324911],"genre_scores_gemma":[0.9996219,0.00001095337,0.0001876423,0.00009283733,0.00003940646,0.000003517538,9.691873e-7,2.504814e-7,0.00004251567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.932812,"threshold_uncertainty_score":0.1300875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01066865669622252,"score_gpt":0.207536592955703,"score_spread":0.1968679362594804,"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."}}