Genome Editing and Rice Improvement: The Role of CRISPR/Cas9 in Developing Superior Yield Traits
Bibliographic record
Abstract
The study demonstrated that the CRISPR/Cas9 system is highly efficient in rice, with nearly half of the target genes edited in the first generation of transformed plants (T0). The mutations were found to be heritable, following classic Mendelian inheritance patterns, with no detectable large-scale off-target effects. Additionally, the CRISPR/Cas9 system enabled high-efficiency multiplex genome editing, allowing for the simultaneous targeting of multiple genes, which is crucial for improving complex traits such as yield. The use of CRISPR/Cas9 has also been shown to enhance grain quality and other agronomic traits, making it a versatile tool for rice improvement. The findings underscore the potential of the CRISPR/Cas9 system as a powerful and precise tool for rice genome engineering. By enabling targeted and heritable gene modifications with minimal off-target effects, CRISPR/Cas9 can significantly contribute to the development of rice varieties with superior yield traits. This technology holds promise for addressing global food security challenges by improving rice productivity and quality.
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 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".