Basis for Selecting Soft Wheat for End‐Use Quality
Bibliographic record
Abstract
ABSTRACT Within the United States, end‐use quality of soft wheat (Triticum aestivum L.) is determined by several genetically controlled components: milling yield, flour particle size, and baking characteristics related to flour water absorption. In 2007 and 2008, we measured the soft wheat quality of 187 soft winter wheat cultivars, released from 1801 to 2005, for the eastern United States. Wheat cultivars were grown in nine eastern United States environments. Quality traits included test weight, flour yield, softness equivalent (an estimator of break flour yield), flour protein concentration, solvent retention capacity (SRC) of flour, and sugar‐snap cookie quality. All of the traits had large variance components due to genotype. Flour milling characteristics had the largest ratio of genotype variance to genotype × environment interaction variance. Based on multivariate analysis of the trait correlation structure, breeders should focus on milling yield, flour softness equivalent, and sucrose SRC, as they predict long‐flow flour milling performance and have value for commercial milling and baking. These traits also have large genetic variance relative to genotype × environment interactions and represent distinct aspects of quality. Although some improvement in soft wheat milling and baking quality has been observed over the past 200 yr, the dominant effect of selection appears to be a stable standard of quality that is associated with the soft wheat classes of the eastern United States.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".