Creating “Plates” to Evaluate Canadians’ Dietary Intake in Relation to the 2019 Canada’s Food Guide
Bibliographic record
Abstract
PURPOSE: (CFG) Plate using novel volume-based food analyses, by age and meal occasion. METHODS: Foods reported in 24-hour recalls by 20,456 Canadians in the 2015 Canadian Community Health Survey - Nutrition were classified as: Vegetables and Fruits, Whole Grain Foods, Protein Foods, Non-Whole Grain Foods or Other Foods (high in fat, sugar, sodium). Food volumes were used to calculate percent contributions of each grouping to total intake, stratified by age (1-6; 7-12; 13-17; 18-64; 65+years) and meal (breakfast, lunch, supper, snack), applying sample survey weights and bootstrapping. RESULTS: By volume, the Canadian population diet included: 29% Vegetables and Fruits, 22% Protein Foods, 7% Whole Grains, 24% Non-Whole Grain Foods, and 18% Other Foods. Intakes of Protein Foods (1-6 years) and Other Foods (7-12; 13-17 years) were higher in children than adults by volume, relative to total intake. Whole Grains intake was highest at breakfast. Other Foods intake was highest at snack. CONCLUSIONS: The volume-based population diet of Canadians reported on a single day includes a substantial proportion of non-recommended foods. There are opportunities to design interventions that target specific foods, ages, and meals to align intake with recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".