{"id":"W2094838394","doi":"10.2135/cropsci2000.40139x","title":"Developing High‐Protein, High‐Yield Soybean Populations and Lines","year":2000,"lang":"en","type":"article","venue":"Crop Science","topic":"Soybean genetics and cultivation","field":"Agricultural and Biological Sciences","cited_by":168,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"High protein; Biology; Maple; Cultivar; Yield (engineering); Reciprocal cross; Agronomy; Horticulture; Breeding program; Botany; Food science; Hybrid","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":[0.0001560187,0.00007503242,0.00006465609,0.00001202914,0.0006350832,0.0001655162,0.0002000682,0.00003369359,0.0004244525],"category_scores_gemma":[0.00005990748,0.00003062325,0.00001407492,0.0005644213,0.0002162858,0.0001670183,0.00004220759,0.00004214584,0.00003418933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001515551,"about_ca_system_score_gemma":0.00001633909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000394947,"about_ca_topic_score_gemma":0.000185469,"domain_scores_codex":[0.9992092,0.000009639407,0.0001201783,0.0002677186,0.0001909003,0.0002023647],"domain_scores_gemma":[0.9997503,0.00002124468,0.00003175459,0.00004735949,0.00007628143,0.00007304046],"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":[0.000003970825,0.00001666261,0.004992445,0.000002089582,0.000001020331,8.593586e-7,0.00007829198,0.00001895531,0.7279298,0.01348443,0.00007823516,0.2533932],"study_design_scores_gemma":[0.00004849823,0.00005827106,0.9606215,0.00002487382,0.000002357561,0.000002340639,0.00008278706,0.000153428,0.0281732,0.007966493,0.00270133,0.0001648801],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971235,0.00005043764,0.00002802199,0.002135206,0.00007584278,0.0001021513,0.000005035921,0.00003925828,0.0004405587],"genre_scores_gemma":[0.996929,0.0000131369,0.002111821,0.0002369304,0.0001318276,0.00000613485,0.000007809245,4.828576e-7,0.0005628806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9556291,"threshold_uncertainty_score":0.4884611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05198514739458943,"score_gpt":0.2522921268078648,"score_spread":0.2003069794132754,"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."}}