Advanced Genetic Tools for Rice Breeding: CRISPR/Cas9 and Its Role in Yield Trait Improvement
Bibliographic record
Abstract
The advent of CRISPR/Cas9 has revolutionized genetic research, providing rice breeding with unprecedented precision and efficiency in genetic modification. This study synthesizes the current applications and advancements of CRISPR/Cas9 technology in rice breeding, particularly focusing on yield trait improvement. By facilitating targeted gene editing, CRISPR/Cas9 enables the modification of specific genes associated with yield, such as grain size, panicle length, and stress tolerance. Key studies demonstrate its effectiveness in enhancing grain quality and increasing overall yield by editing genes like Grain Size 3 ( GS3 ) and OsSAP . Additionally, the technology’s ability to edit multiple genes concurrently through multiplexing has expedited the development of rice varieties tailored to diverse environmental conditions and agricultural demands. Challenges remain, including regulatory hurdles, off-target effects, and the need for precise delivery systems. However, advancements in base and prime editing are addressing these issues, broadening the scope of CRISPR applications. The integration of CRISPR/Cas9 with traditional breeding methods and functional genomics is also enhancing the precision and speed of developing new rice cultivars. Continued research and interdisciplinary collaboration are essential for leveraging CRISPR/Cas9's full potential to meet global food security challenges.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".