The regulation of protein content and quality in national and international food standards
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
Food regulation aims to protect public health through a safe and nutritious food supply produced by a compliant food industry. Food standards of developed countries generally do not regulate protein content or protein quality because the risk of dietary protein inadequacy in their national populations is very low. Protein is nevertheless regulated for reasons of product quality or protein labelling or to minimise assessed health risks associated with consumption of certain animal- and vegetable-protein foods; analogue products that extend or simulate commonly available animal-protein foods; and special purpose foods such as infant formula and foods, supplementary and medical foods, and foods for weight loss. The extent and approach to protein regulation varies greatly among jurisdictions but where it occurs, it is applied through minimum and sometimes maximum limits on protein content or quality measures or both using an inter-related approach. Protein quality measures range from amino acid profiles and digestibility corrected scores to protein rating, a rat bioassay and reference proteins not further described. Regulatory methods for protein quality determination are referenced to the published scientific literature or developed nationally. Internationally, the Codex Alimentarius regulates the protein content and quality of some foods. The Codex approach varies according to the food but is similar to the approaches used in national and regional food regulation. This paper provides a comparison of the regulation of protein in foods using examples from the food regulations of Australia New Zealand, Canada, the European Union, the United States of America and the Codex Alimentarius.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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