Bibliographic record
Abstract
Advances in nanotechnology are resulting in numerous promising applications for improved food production, processing, packaging, and storage. The safety of nanoscale materials in foods has become an increasingly important issue, both in the US and worldwide. Nanoscale particles in foods can be naturally occurring, intentionally added engineered nanomaterials derived from naturally occurring food components, or may be engineered using materials that are not endogenous to foods. In addition, the presence of nanomaterials in foods may be the result of contamination. To assess the health risk of use of these materials to the consumer, both the potential hazard of the materials and the likely exposure must be considered. Although oral exposure to nanomaterials has not been as intensely investigated as other routes of exposure, recent studies using nanotechnology to improve uptake of nutrients and bioactive components illustrate that pharmacokinetics, such as absorption and distribution, can be altered as compared to microscale materials, thereby potentially changing the potential hazard profile associated with the nutrient or bioactive. Efforts to facilitate international collaboration and information exchange are underway to ensure acceptance and utilization of the many benefits of nanotechnology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".