Genome editing of crops: A renewed opportunity for food security
Bibliographic record
Abstract
Genome editing of crop plants is a rapidly advancing technology whereby targeted mutations can be introduced into a plant genome in a highly specific manner and with great precision. For the most part, the technology does not incorporate transgenic modifications and is far superior to conventional chemical mutagenesis. In this study we bring into focus some of the underlying differences between the 3 existing technologies: classical plant breeding, genetic modification and genome editing. We discuss some of the main achievements from each area and highlight their common characteristics and individual limitations, while emphasizing the unique capabilities of genome editing. We subsequently examine the possible regulatory mechanisms which governments may be inclined to use in assessing the status of genome edited products. If assessed on the basis of their phenotype rather than the process by which they are obtained, these products will be categorized as equivalent to those produced by classical mutagenesis. This would mean that genome edited products will not be subject to the restrictions imposed on genetically modified products, except in some cases where the mutation involves a large sequence insertion into the genome. We conclude by examining the potential of societal acceptance of genome editing technology, reinforced by a scientific perspective on promoting such acceptance.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".