Development of Drought‐Tolerant Canola (<i>Brassica napus</i> L.) through Genetic Modulation of ABA‐mediated Stomatal Responses
Bibliographic record
Abstract
ABSTRACT Canola is one of the most important oilseed crops, and its seed yield and quality are significantly affected by environmental stresses such as drought. The phytohormone abscisic acid (ABA) is induced by drought and triggers stomatal closure to reduce transpiration, which accounts for >90% of water loss in plants. The ABA‐mediated stomatal response is a dosage‐dependent process that can be achieved by either increasing the endogenous ABA concentration or by sensitizing the responsiveness of guard cells to the hormone. We summarize the recent breakthroughs in the understanding of key molecular components that regulate the homeostasis and sensing of ABA, and their potential applications in genetic engineering for drought tolerant canola. In particular, the α and β subunits of protein farnesyltransferase have been identified as negative regulators of ABA‐mediated stomatal responses, and their effectiveness as the targets for engineering drought tolerance and yield protection has been confirmed in canola in the field. Further development of the drought stress tolerance property in the crop will likely have a fundamental impact on its productivity in many regions of the world.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".