{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001947759,0.0001364227,0.0002696741,0.00005607634,0.000277756,0.0002578525,0.001371695,0.00004386487,0.0001975282],"category_scores_gemma":[0.00007004609,0.0000399619,0.0001337529,0.001642136,0.00006002903,0.001094813,0.0004250078,0.0001575118,0.00001209499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000791518,"about_ca_system_score_gemma":0.00004301056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009345681,"about_ca_topic_score_gemma":0.00004870569,"domain_scores_codex":[0.9977795,0.00004842296,0.0004445266,0.0003341662,0.001016502,0.0003768623],"domain_scores_gemma":[0.9987395,0.00009654419,0.0004322379,0.00009994379,0.0004465675,0.0001851618],"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":[0.000007228582,0.00002263346,0.01317537,0.000001609233,0.00005575063,0.000001692943,0.0001277829,0.01578526,0.9647039,0.00002059382,0.00007615025,0.00602201],"study_design_scores_gemma":[0.0001557885,0.0002751845,0.9294062,0.00006921202,0.0002190609,0.0001344483,0.001331937,0.06129243,0.006422296,0.00009734812,0.0003112904,0.0002847879],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987328,0.000109721,0.00004798602,0.0003035577,0.0002872026,0.00009562321,0.00001990709,0.000006400295,0.0003968011],"genre_scores_gemma":[0.9947022,0.00003527038,0.004890302,0.00005357376,0.0002123341,2.145264e-7,0.00001583801,4.007055e-7,0.00008989301],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9582816,"threshold_uncertainty_score":0.2548974,"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."}}