A comparison of the EU regulatory approach to directed mutagenesis with that of other jurisdictions, consequences for international trade and potential steps forward
Bibliographic record
Abstract
A special regulatory regime applies to products of recombinant nucleic acid modifications. A ruling from the European Court of Justice has interpreted this regulatory regime in a way that it also applies to emerging mutagenesis techniques. Elsewhere regulatory progress is also ongoing. In 2015, Argentina launched a regulatory framework, followed by Chile in 2017 and recently Brazil and Colombia. In March 2018, the USDA announced that it will not regulate genome-edited plants differently if they could have also been developed through traditional breeding. Canada has an altogether different approach with their Plants with Novel Traits regulations. Australia is currently reviewing its Gene Technology Act. This article illustrates the deviation of the European Union's (EU's) approach from the one of most of the other countries studied here. Whereas the EU does not implement a case-by-case approach, this approach is taken by several other jurisdictions. Also, the EU court ruling adheres to a process-based approach while most other countries have a stronger emphasis on the regulation of the resulting product. It is concluded that, unless a functioning identity preservation system for products of directed mutagenesis can be established, the deviation results in a risk of asynchronous approvals and disruptions in international trade.
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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 | 0.034 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".