Applications of Dietary Reference Intakes in dietary assessment and planning
Bibliographic record
Abstract
Dietary Reference Intakes (DRIs) are used for assessing and planning diets of individuals and groups. Assessing individual intakes is complicated by the fact that neither the individual's usual nutrient intake nor their individual requirement is known. However, the degree of confidence that intakes are adequate or excessive can be estimated. Assessing diets of groups requires information on the group's usual nutrient intake distribution, which can be obtained by statistically adjusting 1 d intake distributions to remove within-person variability. For most nutrients with an Estimated Average Requirement (EAR), the group prevalence of inadequate intakes can be approximated by the percent whose usual intakes are less than the EAR. However, the prevalence of inadequacy cannot be determined for nutrients with an Adequate Intake (AI). The goals of planning are a low risk (for individuals) or low prevalence (for groups) of inadequate or excessive nutrient intakes. For individuals, these goals are met by planning intakes that meet the Recommended Dietary Allowance (RDA) or AI, are below the Tolerable Upper Intake Level (UL), and fall within the Acceptable Macronutrient Distribution Ranges (AMDRs). For groups, planning involves estimating a "target" usual intake distribution with an acceptably low prevalence less than the EAR and greater than the UL, planning menus to achieve the target distribution, and assessing the results.
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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 | 0.037 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".