Breakfast: Shaping Guidelines for Food and Nutrient Patterns
Bibliographic record
Abstract
Whilst there is extensive literature on the health benefits of a regular breakfast, there are few guidelines to help policy makers to issue specific targets on optimal nutrient intake at breakfast or the selection of foods to attain these targets. The food and nutritional advice on breakfast offered by most governments is confined to simple advice on food servings. The USA and Mexico typify the few countries that have attempted to issue specific nutrient targets for breakfast. However, these simply reflect general nutrient guidelines for adults, adjusted to suit lower energy needs of toddlers and school children. Little guidance is issued on micronutrient intake, and the advice on food choice does not appear to be linked to patterns of nutrient intake. The application of cluster and principal component analysis, which is used to determine the patterns of daily or breakfast food consumption and also link them to nutrient intake, greatly improved our understanding of optimal breakfast choices. Using 6 national nutrition surveys (Canada, Denmark, France, Spain, the UK, and the USA), the International Breakfast Research Initiative has opted to score each individual with a measure of overall daily nutritional quality (based on the nutrient-rich food index). It is hoped that options for the derivation in breakfast nutrient targets and associated food-based guidelines will arise from an analysis of tertiles of this score. Ultimately, meal-based advice will become the basic building block for digitally based personalized dietary analysis and guidelines.
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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 | 0.068 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".