A Brief Review of the Occurrence, Use, and Safety of Food‐Related Nanomaterials
Bibliographic record
Abstract
Nanotechnology and nanomaterials have tremendous potential to enhance the food supply through novel applications, including nutrient and bioactive absorption and delivery systems; ingredient functionality; improved colors and flavors; microbial, allergen, and contaminant detection and control; and food packaging properties and performance. To determine the current state of knowledge regarding the safety of these potential uses of nanomaterials, an appraisal of the published literature on the safety of food-related nanomaterials was undertaken. A method of assessment of reliability of toxicology studies was developed to conduct this appraisal. The review of the toxicology literature on oral exposure to food-related nanomaterials found that the number of studies is limited. Exposure to nanomaterials in the human food chain may occur not only through intentional uses in food manufacturing, but also via uses in agricultural production and carry over from use in other industries. Although a number of analytical methods are useful in physicochemical characterization of manufactured nanomaterials, new methods may be needed to more fully detect and characterize nanomaterials incorporated into foods and in other media. There is a need for additional toxicology studies of sufficient quality and duration on different types of nanomaterials to further our understanding of the characteristics of nanomaterials that affect safety of oral exposure resulting from use in various food applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".