Broadening the GMO risk assessment in the EU for genome editing technologies in agriculture
Bibliographic record
Abstract
Abstract Genome editing techniques, especially the CRISPR/Cas technology, increase the possibilities and the speed of altering genetic material in organisms. So-called genome editing is increasingly being used to achieve agriculturally relevant novel traits and/or genetic combinations in both plants and animals, although predominantly as proof of concept studies, with commercial growing or rearing so far limited to the U.S. and Canada. However, there are numerous reports of unintended effects such as off-target effects, unintended on-target effects and other unintended consequences arising from genome editing, summarised under the term genomic irregularities. Despite this, the searching for genomic irregularities is far from routine in these studies and protocols vary widely, particularly for off-target effects, leading to differences in the efficacy of detection of off-target effects. Here, we describe the range of specific unintended effects associated with genome editing. We examine the considerable possibilities to change the genome of plants and animals with SDN-1 and SDN-2 genome editing (i.e. without the insertion of genes conferring the novel trait) and show that genome editing techniques are able to produce a broad spectrum of novel traits that, thus far, were not possible to be obtained using conventional breeding techniques. We consider that the current EU risk assessment guidance for GMOs requires revision and broadening to capture all potential genomic irregularities arising from genome editing and suggest additional tools to assist the risk assessment of genome-edited plants and animals for the environment and food/animal feed in the EU.
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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 | 0.063 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".