β-diketone accumulation in response to drought stress is weakened in modern bread wheat varieties (Triticum aestivum L.)
Bibliographic record
Abstract
Cuticular waxes coating leaf surfaces can help plants tolerate drought events by reducing non-stomatal water loss. Despite their role in drought tolerance, little is known about how cuticular wax composition has changed during breeding in Canadian bread wheat (Triticum aestivum L.) varieties. To fill in this gap, flag leaves of the Canadian Heritage Bread Wheat Panel, which include 30 varieties released between 1842 and 2018, were surveyed to determine if and how cuticular wax composition in wheat has changed at two breeding ecozones over this period. Following this, a subset of varieties was subjected to drought conditions to compare their responses. As expected, modern varieties outperformed old varieties with a significantly larger head length and reaching maturity earlier. Yet, when challenged with drought, old varieties were able to significantly increase the accumulation of β-diketones to a higher extent than modern varieties. Furthermore, RNAseq was performed on the flag leaf of four modern varieties to identify potential markers that could be used for selection of higher accumulation of cuticular waxes. This analysis revealed that the W1 locus is a good candidate for selecting higher accumulation of β-diketones. These findings indicate that the variation in cuticular waxes upon drought could be further incorporated in breeding of future bread wheat varieties.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".