Sodium Reduction in Processed Foods in Brazil: Analysis of Food Categories and Voluntary Targets from 2011 to 2017
Bibliographic record
Abstract
Non-communicable diseases, including cardiovascular diseases, are responsible for over 70% of deaths in Brazil. Currently, over 25% of Brazilian adults are diagnosed as hypertensive; overall, current dietary sodium intake in Brazil (4700 mg/person) is over twice the international recommendations, and 70-90% of adolescents and adults consume excessive sodium. National sodium reduction strategies consider the main dietary sources of sodium to be added salt to foods, foods consumed outside of the household, and sodium in processed foods. The national voluntary strategy for sodium reduction in priority food categories has been continuously monitored over a 6-year period (2011-2017) and there was a significant 8-34% reduction in the average sodium content of over half food categories. Different food categories have undergone differing reductions in sodium over time, aiding gradual biannual targets to allow industries to develop new technologies and consumers to adapt to foods with less salt. By 2017, most products of all food categories had met the regional targets proposed by the Pan American Health Organization, showing that voluntary sodium reduction strategies can potentially contribute to food reformulation. Nevertheless, regulatory approaches may still be necessary in the future in order to reach all food producers and to allow stronger enforcement to meet more stringent regional targets.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".