Precise Editing and Functional Verification of Pine Disease Resistance Genes
Bibliographic record
Abstract
The primary goal of this study is to explore the precise editing and functional verification of disease resistance genes in pine species, with a focus on leveraging advanced genome editing technologies to enhance disease resistance. Recent advancements in genome editing, particularly the CRISPR/Cas9 system, have enabled precise modifications of disease resistance genes in various plant species, including pines. Studies have demonstrated the successful identification and mapping of resistance genes, such as Cr1 in sugar pine and Cr3 in southwestern white pine, which are crucial for combating diseases like white pine blister rust. Additionally, the use of high-density genetic maps and SNP markers has facilitated the understanding of the genomic architecture underlying disease resistance, revealing the evolutionary pressures and potential for marker-assisted selection in breeding programs. The application of genome editing has also shown promise in creating de novo functional alleles to drive resistance without compromising plant physiology. The integration of genome editing technologies in pine breeding programs holds significant potential for developing disease-resistant varieties. These advancements not only enhance our understanding of the genetic basis of disease resistance but also provide practical tools for breeding and conservation efforts. The findings underscore the importance of continued research and application of genome editing to ensure sustainable forest management and resilience against pathogens.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".