Consumption of Ready-to-Eat Cereal in Canada and Its Contribution to Nutrient Intake and Nutrient Density among Canadians
Bibliographic record
Abstract
In recent years, ready-to-eat cereal (RTEC) has become a common breakfast option in Canada and worldwide. This study used the nationally representative cross-sectional data from the Canadian Community Health Survey (CCHS) 2015-Nutrition to determine patterns of RTEC consumption in Canada and the contribution to nutrient intake among Canadians who were ≥2 years, of whom 22 ± 0.6% consumed RTEC on any given day. The prevalence of RTEC consumption was highest in children aged two to 12 years (37.6 ± 1.2%), followed by adolescents aged 13 to 18 years (28.8 ± 1.4%), and then by adults ≥19 years (18.9 ± 0.6%). RTEC consumers had higher intakes of “nutrients to encourage” compared to the RTEC non-consumers. More than 15% of the daily intake of some nutrients, such as folic acid, iron, thiamin, and vitamin B6, were contributed by RTEC. It was noted that nearly 66% of milk consumption was co-consumed with RTEC among RTEC consumers. The nutrient density of the diet, as defined by Nutrient-Rich Food Index (NRF 9.3), was significantly higher among RTEC consumers compared to non-consumers. RTEC consumption was not associated with overweight/obesity. RTEC consumption considerably contributed to the intake of some key nutrients among all age groups 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".