Bibliographic record
Abstract
Dietary Reference Intakes (DRIs) are nutrient reference standards used for planning and assessing the diets of apparently healthy Canadians and Americans. The development of DRIs reflects a joint initiative by the United States and Canada to update, expand on, and replace the former Recommended Nutrient Intakes for Canadians and Recommended Dietary Allowances for Americans. DRIs include the Estimated Average Requirement (EAR), Recommended Dietary Allowance (RDA), Adequate Intake (AI), and Tolerable Upper Intake Level (UL). The EAR is the average daily intake level that meets the requirement of 50% of healthy individuals in a life stage and gender group, whereas the RDA is set at a level that will meet the requirements of almost all (97%-98%) individuals in that life stage and gender group. An AI is a recommended intake level that is thought to meet the needs of almost all healthy individuals, and is set when there are insufficient data to establish an EAR (and therefore an RDA). The UL represents a threshold above which adverse effects of excessive intake may increase. In addition to these DRIs, macro nutrients have an Acceptable Macro nutrient Distribution Range (AMDR) and, for energy, an Estimated Energy Requirement (EER) is described.
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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 0.034 |
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".