Calories, portion size and caloric density ‐ implications for restaurant calorie labeling
Bibliographic record
Abstract
The increasing trend towards eating-out along with concerns about the adverse nutritional profile of restaurant foods has prompted the introduction of calorie labeling. However, the role of caloric density and portion size as determinants of calories in restaurants has not been analyzed. The objective of this study was to evaluate how the type of food, the type of establishment, serving size, and caloric density influence calories; and the implications of this for calorie labeling. Data was collected from the major (n=85) sit-down and quick-service restaurants across Canada in 2010. 4167 side dishes, main entrées and individual items were analyzed. There was substantial variation in calories both within and across food categories. In most categories, sit-down restaurants had significantly higher calories compared to quick-service restaurants (p<0.05). While portion size explained 37% of the variation in calories, caloric density explained only 5%. Nevertheless, both were statistically significant predictors of calories (p<0.05). Higher calorie items had a significantly larger portion size compared to lower calorie items, but were not always significantly different in terms of caloric density. Thus, calorie labeling may undermine weight-loss efforts by leading customers to choose lower calorie food items that are smaller in portion size, but not necessarily lower in caloric density. Funding: CIHR STIHR Comparing the Average Serving Size and Caloric Density of Restaurant Items Differing in Calories Grant Funding Source : University of Toronto McHenry Chair
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".