Estimating Serving Sizes for Healthier and Unhealthier Versions of Food According to Canada’s Food Guide
Bibliographic record
Abstract
PURPOSE: Canada's Food Guide (CFG) defines food serving sizes and recommends a specific number of servings from each of the 4 food groups. However, there is no differentiation in serving sizes for different versions of foods that may differ in nutritional value. METHODS: Participants (n = 20) estimated serving sizes of "healthier" and "unhealthier" versions of milk, bread, cereal, potatoes, chicken, fish, and juice and reported the amount normally consumed in 1 sitting. RESULTS: Participants estimated unhealthier servings of cereal and juice to be smaller than healthier servings, but estimated unhealthier servings of chicken to be larger than healthier versions (P < 0.05). There were no differences for bread, milk, potatoes, and fish. Accordingly, estimated servings of juice (P < 0.01) had more calories than the unhealthier orange drink. There were no caloric differences for cereal (P = 0.12), but an estimated serving of bran flakes had more fat and fibre than frosted flakes cereal. CONCLUSIONS: In contrast with CFG, which does not account for different versions of food, certain unhealthier foods were estimated to be smaller or larger than the healthier versions. However, both healthy and unhealthy serving sizes still tended to be larger than what is prescribed in CFG. Thus, better education or revision of serving sizes in future editions of CFG may warrant consideration.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".