Editorial: CRISPR-based genome editing for seed oil improvements in Brassica napus L.
Bibliographic record
Abstract
Editorial on the Research Topic CRISPR-based genome editing for seed oil improvements in Brassica napus L.Rapeseed (Brassica napus L., AACC, 2n = 38) is the world's third-significant oilseed crop after soybean and oil palm, renowned for its high-quality edible oil and biofuel production (USDA ERS, 2021).The demand for rapeseed in various industries continues to surge, necessitating advancements in genetic traits to meet market requirements.Enhancing crop traits, both quantitatively and qualitatively, has always remained a focal point for agricultural researchers.Conventional and molecular approaches have been employed in the past; however, they are often time-consuming, lack precision and may result in genetic instability of desirable breeding traits.Recent advancements in genome editing technology, specifically Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-associated (Cas) proteins, have revolutionized the field of plant breeding.This editorial delves into the potential of CRISPR technology in augmenting Brassica's seed oil and fatty acid composition, as evidenced by publications featured in this Frontiers' Research Topic titled "CRISPR-Based Genome Editing for Seed Oil Improvements in Brassica napus L." By meticulously examining five publications, including one minireview, and four research articles, this editorial aims to inspire researchers to embrace this revolutionary approach for rapeseed oil improvement and genetic enhancement.The insights presented here aim to emphasize CRISPR technology's significance in empowering researchers towards achieving sustainable and enhanced agricultural practices.In this editorial, we summarize the key findings and perspectives outlined in each of the accepted articles.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.028 | 0.017 |
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".