Should Canadians eat according to the traditional Mediterranean diet pyramid or Canada’s food guide?
Bibliographic record
Abstract
Eating well with Canada's food guide (CFG) was developed by Health Canada as an education tool to encourage the Canadian public to have eating habits that meet nutrient needs, promote health, and reduce the risk of nutrition-related chronic disease. It was developed in the Canadian context and reflects the food supply available to Canadians, as well as food choices made by Canadians. There are other dietary patterns that are consistent with health such as the traditional Mediterranean diet (TMD), which has gained popularity in Canada. The potentially different food choices that Canadians could make if they were to follow one guide over the other might significantly influence population health. Although the two guides differ in their recommendations for red wine, fats, and meat and meat alternatives, they both promote a diet rich in grains, fruits, and vegetables. The CFG may have some advantages over the TMD for Canadians, such as focusing on vitamin D and recommending limited alcoholic beverage intake. Some shortcomings of the CFG compared with the TMD are the grouping of animal proteins with nuts, seeds, and legumes into a single category, and not recommending limits for red meat consumption. If Canadians following the CFG were to choose whole grains and vegetarian options from the meat and alternatives category more often, the CFG may be preferable to TMD for Canadians. The TMD is an alternative to the CFG for Canadians if sources of vitamin D are included in the diet and wine consumption is limited or is imbibed in moderation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".