Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Risk assessment of packaging materials provides a unique challenge. Human exposure to packaging materials and/or their components occurs from migration into foods. There are various methods for determining migration into foods. Unlike most food additives, these exposures typically are very small. Because of this, and since complete toxicological data sets are not always available for packaging materials, the US Food and Drug Administration (FDA) has developed a process to make the evaluation of packaging materials more efficient, instead of the extensive review normally required for food additives. This process is used to determine 'when the likelihood or extent of migration to food of a substance used in a food-contact article is so trivial as not to require regulation of the substance as a food additive'. This trivial level, also known as the threshold of regulation, was based upon a large database of carcinogenic potencies and was determined to be 1.5 microg/person day(-1). This was determined to 'be low enough to ensure that the public health is protected, even in the event that a substance exempted from regulation as a food additive is later found to be a carcinogen'. Substances not having structural alerts, or that are not known carcinogens or potent toxins, based on existing toxicological information, and are below the threshold value, are considered by the FDA to be exempted from regulation as food additives. The threshold of regulation approach used by the FDA provides an excellent model by which to evaluate the majority of packaging materials.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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 it