{"id":"W1762601300","doi":"10.5539/jas.v7n9p154","title":"Selection for High Yield and Stability among Early Maturing Greengram Genotypes","year":2015,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"World Bank Group","keywords":"Radiata; Yield (engineering); Vigna; Biology; Selection (genetic algorithm); Genotype; Gene–environment interaction; Stability (learning theory); Biotechnology; Agronomy; Statistics; Horticulture; Mathematics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008625204,0.00008783172,0.0001314277,0.00001603369,0.0002514097,0.0002037966,0.0002280215,0.00004934552,0.000009522707],"category_scores_gemma":[0.0002020525,0.00002703808,0.00004185852,0.0003701064,0.0001037409,0.0004503789,0.00004727853,0.0001143499,7.725288e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000417005,"about_ca_system_score_gemma":0.00001696621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004110481,"about_ca_topic_score_gemma":0.00051486,"domain_scores_codex":[0.9990302,0.00001441558,0.0002183393,0.0001559281,0.0003513494,0.000229777],"domain_scores_gemma":[0.9989522,0.0001119876,0.0002028493,0.0000166738,0.0004938994,0.0002224463],"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":[0.00002426446,0.00001871834,0.1022896,0.000003679199,0.000004586983,5.026918e-7,0.0001776637,0.0000165343,0.8903806,0.00009520593,0.0001845056,0.006804246],"study_design_scores_gemma":[0.00007806723,0.0005728509,0.9495325,0.00001924852,0.00001031103,0.00005284108,0.0004028791,0.00001799061,0.04872366,0.0004043332,0.00009519652,0.00009011062],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989484,0.00007928297,0.00000859584,0.0005137475,0.0002468281,0.0001152867,0.000006202318,0.000009728354,0.00007193415],"genre_scores_gemma":[0.9989885,0.000015933,0.0005717843,0.00001712345,0.000378465,0.000001339676,0.000001155515,3.199993e-7,0.00002536751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.847243,"threshold_uncertainty_score":0.1965216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04237903178055148,"score_gpt":0.2077449300838383,"score_spread":0.1653658983032868,"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."}}