Urban Food Sources and the Challenges of Food Availability According to the Brazilian Dietary Guidelines Recommendations
Bibliographic record
Abstract
The study investigated availability and food sources in urban areas using elements of the NOVA food classification system, adopted by the Brazilian Dietary Guidelines, in a Brazilian municipality. In addition, the study also aimed to identify inequalities in the geographical distribution of food retailers that commercialize healthy and/or unhealthy foods. This cross-sectional study was performed in the municipality of Jundiai in the State of São Paulo, Brazil. Data from within-store audit and geographic data were used to characterizing the nutrition community environment. The mean was calculated for food items available in each of the four NOVA groups for each audited food retailer. The mean of food items available in each of the four NOVA groups for each audited food retail were calculated. The density and proportion of different types of food retailers were georeferenced. The supermarkets, medium market stores, and grocery stores presented the highest availability of unprocessed foods as well as ultra-processed foods. Establishments that sold primarily unprocessed foods and included a fruits and vegetables section at the entrance of the store had a greater availability of healthy foods, but their density in the territory was low compared to establishments that prioritized the sale of ultra-processed foods and sold ultra-processed foods in the checkout area. Especially in middle- and low-income areas, the concentration of food retailers with priority sale of ultra-processed products is reaches 22 times higher than the sale of unprocessed or minimally processed foods. The study supported the identification of regions where it was necessary to improve access to equipment that marketed unprocessed foods as a priority.
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How this classification was reachedexpand
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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".