The effect of genotype and growing environment on the gluten strength and end-use quality of CWRS wheat
Bibliographic record
Abstract
The goal of this study was to develop a comprehensive understanding of genotype (G) and environmental (E) influences on gluten strength which is a critical asset of CWRS wheat. Replicated field trials were carried out in two years involving nine CWRS cultivars grown in nine locations. Wheat, flour, dough and protein properties were evaluated. Gluten strength was measured using a 10 g mixograph and further analyzed in relation to protein content (PC) of flour, soluble prolamins (SP) and HMW glutenin (HMWG). Results indicated gluten strength was unrelated to total FP, inversely related to SP/FP, and strongly associated with HMWG/FP or HMWG/SP. Genotype and growing environment weather were the primary and secondary contributors, respectively, to total variance in gluten strength. A novel analysis of weather parameters across field sites yielded multivariate regression models that explained > 90% of variation in PC and key measures of gluten strength. Genotype ranking of gluten strength was not completely consistent with recent decisions by the Canadian Grain Commission to retain or drop cultivars from the CWRS class. The study highlighted many advantages of using a G x E approach to evaluate gluten strength and other attributes of CWRS wheat, especially to sort out the relative contributions of G and E for this important wheat class.
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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 | 0.000 | 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".