Estimating the Sugars Content of Diets that Follow <i>Eating Well with Canada’s Food Guide</i>
Bibliographic record
Abstract
PURPOSE: This research aimed to estimate the percent energy (%E) contribution from total and free sugars in the Eating Well with Canada's Food Guide (CFG) dietary pattern. METHODS: The sugar-containing foods in the Canadian Nutrient File were assigned to 1 of 2 categories: total sugars or free sugars based on the source. The total sugars content of foods containing any amount of free sugars was assigned to the free sugars category. We estimated free sugars content from 8000 simulated diets (500 for each of the 16 age and sex groups), consistent with the CFG dietary pattern. Descriptive statistics were used to examine distributions of %E from total and free sugars by age and sex. RESULTS: The mean %E from total and free sugars of all simulated diets was 21%E and 7%E, respectively. For simulated diets for males and females, 9-18 years of age, the %E from free sugars exceeded 10% at the 75th percentile. Simulated diets for all other age and sex groups exceeded 10%E from free sugars at the 95th percentile. CONCLUSIONS: The majority of the simulated CFG diets met the WHO recommendations to limit free sugars consumption to <10%E. These results will be used to inform future dietary guidance policy development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".