A Comparative Study of the Sodium Content and Calories from Sugar in Toddler Foods Sold in Low- and High-Income New York City Supermarkets
Bibliographic record
Abstract
Information from the nutrition facts labels of toddler foods marketed in low- and high-income New York City zip codes were analyzed for sodium content, the proportion of sugar-derived calories, and presence of sugar and/or high-fructose corn syrup as an added sweetener in the list of ingredients. Among the 272 toddler foods analyzed, more than a quarter were high in sodium, over one-third derived at least 20% their calories from sugar, and more than 41% of the foods had sugar and/or high-fructose corn syrup listed among the first five ingredients. The proportion of foods with such nutritional characteristics did not significantly differ between the low- and high-income neighborhood supermarkets. Median sodium content was highest among "side dishes" and "meals." The proportion of calories derived from sugar was found to be highest among "snacks and yogurt blends" in both low- and high-income neighborhoods and "breakfast foods and cereals" in low-income neighborhoods. When compared to high-income neighborhoods, more than three times the proportion of total calories in "breakfast foods and cereals" sold in low-income neighborhoods were derived from sugar. Since taste preferences established during childhood can have long-lasting influence on dietary habits, it is imperative to limit the promotion of toddler foods that are high in sodium and sugar as well as educate parents to make nutritionally sound decisions at the point of purchase.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".