The impact of voluntary targets on the sodium content of processed foods in Brazil, 2011–2013
Bibliographic record
Abstract
Brazilians consume excessive dietary sodium (4700 mg/d); hence, the reduction of dietary sodium intake has been a Brazilian government priority. A set of strategies has been implemented that includes food and nutrition education initiatives and the reduction in the sodium content of processed foods and foods consumed out of the households. Since 2011, the Ministry of Health has selected priority food categories that contribute to over 90% of sodium intake from processed foods and have set biannual voluntary targets for sodium reduction with food industries to encourage food reformulation. Three rounds of monitoring of the sodium content on food labels have been conducted for instant pasta, commercially produced breads, cakes and cake mixes, cookies and crackers, snacks, chips, mayonnaise, salt-based condiments, and margarine. Between 90% and 100% of the food products achieved the first targets in the 2011-2013 period, and the average sodium content of food categories was reduced from 5% to 21% in these first 2 years. These data show that with close monitoring and government oversight, voluntary targets to reduce the sodium content in processed foods can have a significant impact even in a short time frame. The Brazilian strategy will be continuously monitored to maximize its impact, and, if necessary in the future, a transition to regulatory approaches with stronger enforcement may be considered.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".