{"id":"W2943384073","doi":"10.5539/jas.v11n6p22","title":"Evaluating Winter Barley Cultivar Using Data Envelopment Analysis Models","year":2019,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cultivar; Mathematics; Brewing; Yield (engineering); Ranking (information retrieval); Grain yield; Row; Grain quality; Agronomy; Statistics; Horticulture; Biology; Computer science; Food science; Database; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005043346,0.001141504,0.001049549,0.00129219,0.0003211204,0.00172069,0.0004827809,0.0007214583,0.0008038193],"category_scores_gemma":[0.00771235,0.0004336075,0.001317408,0.0008151873,0.0002514847,0.000916057,0.0006589596,0.0007141678,0.0001795896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001471974,"about_ca_system_score_gemma":0.001410626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01076988,"about_ca_topic_score_gemma":0.00539512,"domain_scores_codex":[0.9986192,0.000874117,0.0000833501,0.0001517331,0.0001565589,0.0001150562],"domain_scores_gemma":[0.9941573,0.004836631,0.0003411396,0.0001396452,0.0004376006,0.00008761357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001238953,0.00006849747,0.004040114,0.00004388799,0.0001082065,0.00002513221,0.00004271877,0.9789922,0.0007255943,0.001006313,0.0001337266,0.01468976],"study_design_scores_gemma":[0.000003595616,0.00008005318,0.0007674127,0.00000547665,0.00001092589,0.000004578667,0.00002134942,0.9980869,0.000310349,0.0005850726,0.0001173751,0.000006917353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5234548,0.0006311171,0.472188,0.0002513838,0.00002197043,0.0001827887,0.0004613078,0.0003305051,0.002478211],"genre_scores_gemma":[0.949708,0.000181278,0.04857612,0.00002872162,0.000005250608,0.0001376859,0.0004661604,0.00003570246,0.0008610971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01076988,"threshold_uncertainty_score":0.02667207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1782862539711033,"score_gpt":0.3180738703166865,"score_spread":0.1397876163455832,"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."}}