Comparing the nutritional composition of foods and beverages in the Canadian Nutrient File to a large representative database of Canadian prepackaged foods and beverages
Bibliographic record
Abstract
BACKGROUND: Nutrient information used to code dietary intakes in the Canadian Community Health Survey (CCHS) may not be reflective of the current Canadian food supply and could result in inaccurate evaluations of nutrient exposures. OBJECTIVE: To compare the nutritional compositions of foods in the CCHS 2015 Food and Ingredient Details (FID) file (n = 2,785) to a large representative Canadian database of branded food and beverage products (Food Label Information Program, FLIP) collected in 2017 (n = 20,625). METHOD: Food products in the FLIP database were matched to equivalent generic foods from the FID file to create new aggregate food profiles based on FLIP nutrient data. Mann Whitney U tests were used to compare nutrient compositions between the FID and FLIP food profiles. RESULTS: In most food categories and nutrients there were no statistically significant differences between the FLIP and FID food profiles. Nutrients with the largest differences included: saturated fats (n = 9 of 21 categories), fiber (n = 7), cholesterol (n = 6), and total fats (n = 4). The meats and alternatives category had the most nutrients with significant differences. CONCLUSION: These results can be used to prioritize future updates and collections of food composition databases, while also providing insights for interpreting CCHS 2015 nutrient intakes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.022 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".