International collaborative project to compare and monitor the nutritional composition of processed foods
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
BACKGROUND: Chronic diseases are the leading cause of premature death and disability in the world with overnutrition a primary cause of diet-related ill health. Excess energy intake, saturated fat, sugar, and salt derived from processed foods are a major cause of disease burden. Our objective is to compare the nutritional composition of processed foods between countries, between food companies, and over time. DESIGN: Surveys of processed foods will be done in each participating country using a standardized methodology. Information on the nutrient composition for each product will be sought either through direct chemical analysis, from the product label, or from the manufacturer. Foods will be categorized into 14 groups and 45 categories for the primary analyses which will compare mean levels of nutrients at baseline and over time. Initial commitments to collaboration have been obtained from 21 countries. CONCLUSIONS: This collaborative approach to the collation and sharing of data will enable objective and transparent tracking of processed food composition around the world. The information collected will support government and food industry efforts to improve the nutrient composition of processed foods around the world.
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