Engineered Nanoparticles and Food: Exposure, Toxicokinetics, Hazards and Risks
Bibliographic record
Abstract
With the increasing use of nanomaterials in food, we need to ask whether this poses a risk to the workers manufacturing the nanomaterials and/or consumers. Society expects safe ingredients to be used, especially for applications in food. This chapter considers the use of nanomaterials in food and what information can be used to evaluate the safety aspects of engineered nanoparticles. Any risk assessment starts with a characterization of the (nano)materials to be evaluated. This is especially important for nanomaterials because a large number of variations in their physicochemical properties are possible, which can modify their functionality and behaviour. Current basic risk assessment procedures for classical chemical substances can also be applied to the safety evaluation of nanomaterials. This approach is based on exposure assessment, hazard identification (what causes the hazard or toxic effect), hazard characterization (what is the toxic effect and the dose–response relation) and risk characterization, which describes the relationship between human exposure and the dose that induces a toxic effect in experimental studies. Aspects specific to nanoparticles have to be taken into account. Recent insights into the tissue distribution of engineered nanoparticles and modelling of the exposure of internal organs are suggested as alternative approaches to the risk assessment of engineered nanoparticles.
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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 | 0.000 | 0.000 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".