Bibliographic record
Abstract
Dietary Reference Intakes (DRIs) are nutrient standards that may be used to plan nutrient intakes. Thus, they are useful as the basis for formulating dietary guidelines. The guidelines are often presented to the public as a food guide that will promote nutrient adequacy without risk of excessive intake. Such guides typically use the Recommended Dietary Allowances (RDAs) as intake targets because intake at the RDA is associated with a high probability of nutrient adequacy for healthy persons. During the development of the MyPyramid food guide for the United States, several questions were addressed: (1) What energy levels will be covered by the specific food patterns within the food guide? Each pattern should promote nutrient adequacy for the targeted energy intake level, which may include different age and gender groups. (2) What nutrients will be targeted by the food patterns? They should promote nutrient adequacy while also ensuring that intakes are not excessive for food components such as sodium, saturated fat, and cholesterol. (3) What food groups will be included in the food patterns, and how will their nutrient profiles be determined? After these decisions have been made, then the recommended amounts of each food group can be determined. A unique approach has been used to develop Canada's Food Guide, which included a simulation of the effect of differing food choices within each food group. Dietary guidelines and food guides which are based on the DRIs have the potential to improve nutrient intakes for consumers who follow them.
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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".