Evaluating Winter Barley Cultivar Using Data Envelopment Analysis Models
Bibliographic record
Abstract
Unlike feed barley, malting barley must meet a specific set of quality standards for acceptability by maltsters. Multiple quality criteria in addition to the grain yield makes ranking of genotypes challenging. The objective of this study was to apply data envelopment analysis (DEA) models to rank the efficiencies of 27 winter barley entries based on grain yield and quality indices. Four methods of DEA including Charnes, Cooper and Rhodes (CCR), Färe and Grosskopf (FG), Banker, Charnes and Cooper (BCC), and Seiford and Thrall (ST) used for the ranking. Testing trial included 14 two-rows and 13 six-rows winter barley. All entries except two, demonstrated high winter survival ratings. Overall, the six-row cultivars out-yielded the two-row cultivars by 18%. However, in terms of brewing quality, the two-row entries performed better than six-row entries and had 40% lower thinness, 12% higher plump, and lower grain protein content. The six-row entries had 32% higher germinative energy than two-row entries. The ranking by four models were not similar, however, SU-Mateo and Calypso had the highest efficiency (1.0) by all four models followed by Wintmalt and Vincenta.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".