Development of a Canadian Food Composition Database of Gluten-Free Products
Bibliographic record
Abstract
Country-specific food composition data are needed for gluten-free (GF) food products to assess nutritional adequacy and diet quality. This research aimed to develop a comprehensive GF food composition database for key GF foods consumed in Canada. Average nutrient data from 167 products were estimated from Nutrition Fact Panel labels and the commercial ingredient list, using an iterative and systematic approach. The database reports mean values for energy and 29 nutrients per 100 g for 33 GF commercial grain-based foods. Nutrient values were evaluated with Health Canada's nutrient content claims per standard reference serving. On average, GF products were, at minimum, a source of thiamin (73%), riboflavin (70%), niacin (58%), iron (58%), fibre (55%), magnesium (48%), folate (36%), zinc (19%), and calcium (15%). Most GF products were low in saturated fat (85%) and cholesterol (64%) but only 15% were low in total fat and 6% were free of sugar. Micronutrient enrichment and the use of nutrient-dense whole grain flours, legume flours, oil seed husks, and functional fibre ingredients varied within and between categories and brands but appeared to contribute to nutrient content. This database provides a new tool to enhance GF diet assessment in individuals or populations in Canada.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".