Application of CRISPR/Cas9 Technology in Editing Poplar Drought Resistance Genes
Bibliographic record
Abstract
Poplars (genus Populus ), as fast-growing deciduous trees, hold significant ecological and economic importance. They contribute to carbon sequestration, soil stabilization, and provide habitats for wildlife, while also being widely used in timber, paper production, and bioenergy. However, drought stress poses a major challenge to the growth, productivity, and survival of poplars. The advent of CRISPR/Cas9 technology has revolutionized plant genetic engineering, offering precise and efficient genome editing capabilities. This study systematically introduces the basic principles of CRISPR/Cas9 technology and its applications in plant science, particularly how editing key drought-resistance genes can enhance the drought tolerance of poplars. Additionally, we discuss practical applications of CRISPR/Cas9-mediated gene editing in poplars, as well as challenges such as off-target effects, regulatory and ethical considerations, and environmental and ecological impacts. In the future, continuous innovation in CRISPR/Cas9 technology and its combination with other biotechnological methods are expected to further improve the drought resistance of poplars, promoting their commercial application and large-scale deployment. This study not only provides new approaches for improving the drought resistance of poplars but also offers valuable references for the research and application of stress resistance in other forest species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".