Stress-induced expression in wheat of the <i>Arabidopsis thaliana</i> DREB1A gene delays water stress symptoms under greenhouse conditions
Bibliographic record
Abstract
One of the major environmental factors limiting plant productivity is lack of water. This is especially true for the major cereals maize, rice, and wheat, which demonstrate a range of susceptibility to moisture deficit. Although conventional breeding and marker-assisted selection are being used to develop varieties more tolerant to water stress, these methods are time and resource consuming and germplasm dependent. Genetic engineering is attractive because of its potential to improve abiotic stress tolerance more rapidly. Transcription factors have been shown to produce multiple phenotypic alterations, many of which are involved in stress responses. DREB1A, a transcription factor that recognizes dehydration response elements, has been shown in Arabidopsis thaliana to play a crucial role in promoting the expression of drought-tolerance genes. In our efforts to enhance drought tolerance in wheat, the A. thaliana DREB1A gene was placed under control of a stress-inducible promoter from the rd29A gene and transferred via biolistic transformation into bread wheat. Plants expressing the DREB1A gene demonstrated substantial resistance to water stress in comparison with checks under experimental greenhouse conditions, manifested by a 10-day delay in wilting when water was withheld.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".