Regulatory aspects of nanotechnology in the agri/feed/food sector in EU and non-EU countries
Bibliographic record
Abstract
Nanotechnology has the potential to innovate the agricultural, feed and food sectors (hereinafter referred to as agri/feed/food). Applications that are marketed already include nano-encapsulated agrochemicals or nutrients, antimicrobial nanoparticles and active and intelligent food packaging. Many nano-enabled products are currently under research and development, and may enter the market in the near future. As for any other regulated product, applicants applying for market approval have to demonstrate the safe use of such new products without posing undue safety risks to the consumer and the environment. Several countries all over the world have been active in examining the appropriateness of their regulatory frameworks for dealing with nanotechnologies. As a consequence of this, different approaches have been taken in regulating nano-based products in agri/feed/food. The EU, along with Switzerland, were identified to be the only world region where nano-specific provisions have been incorporated in existing legislation, while in other regions nanomaterials are regulated more implicitly by mainly building on guidance for industry. This paper presents an overview and discusses the state of the art of different regulatory measures for nanomaterials in agri/feed/food, including legislation and guidance for safety assessment in EU and non-EU countries.
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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 | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".