Measurement Methods to Evaluate Engineered Nanomaterial Release from Food Contact Materials
Bibliographic record
Abstract
This article is one of a series of 4 that report on a task of the NanoRelease Food Additive project of the Intl. Life Science Inst. Center for Risk Science Innovation and Application to identify, evaluate, and develop methods that are needed to confidently detect, characterize, and quantify intentionally produced engineered nanomaterials (ENMs) released from food along the alimentary tract. This particular article focuses on the problem of detecting ENMs that become released into food indirectly from food contact materials. In this review, an in-depth analysis of the release literature is presented and relevant release mechanisms are discussed. The literature review includes discussion of articles related to the release phenomenon in general, as experimental methods to detect ENMs migrating from plastic materials into other (nonfood) complex matrices were determined to be relevant to the focus problem of food safety. From the survey of the literature, several "control points" were identified where characterization data on ENMs and materials may be most valuable. The article concludes with a summary of findings and a discussion of potential knowledge gaps and targets for method development in this area.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".