Nutritional Quality of Food and Beverages Offered in Supermarkets of Lima According to the Peruvian Law of Healthy Eating
Bibliographic record
Abstract
The purpose of this paper was to determine the foods and beverages offered in the city of Lima, Peru, that would be subject to front-of-package warning labels (octagons) according to the thresholds for the two phases (6 and 39 months after the approval) for nutrients of concern (sugar, sodium, saturated fat, and trans-fat) included in the Peruvian Law of Healthy Eating. An observational, descriptive cross-sectional study was conducted that evaluated the nutritional composition of processed and ultra-processed foods that are sold in a supermarket chain in Lima. Of all the processed and ultra-processed foods captured, foods that report nutritional information and do not require reconstitution to be consumed were included. A descriptive analysis was carried out by food categories to report the nutrient content and the percentage of foods that would be subject to front-of-package warning labels. Results: A total of 1234 foods were evaluated, according to the initial thresholds that became effective 6 months after the law was implemented; 35.9% of foods had two octagons; 34.8% had one octagon; 15.8% had no octagons; 12% had three octagons; and no products had four octagons. At 39 months, when the final and more restrictive thresholds become effective, 4.8% did not have octagons. The majority of processed and ultra-processed foods that are sold in a Peruvian supermarket chain carry at least one octagon, and more than 10% of them carry octagons for three of the four nutrients of concern.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".