Dietary Reference Intakes: Guiding Principles for Nutrition Labeling and Fortification
Bibliographic record
Abstract
The following is the executive summary from the report Dietary Reference Intakes: Guiding Principles for Nutrition Labeling and Fortification, prepared by Committee on Use of Dietary Reference Intakes in Nutrition Labeling of the Institute of Medicine, National Academies: Irwin H. Rosenberg (Chair), Steven A. Abrams, Gary R. Beecher, Catherine M. Champagne, Fergus M. Clydesdale, Jeanne P. Goldberg, Penny M. Kris-Etherton, Jerold R. Mande, George P. McCabe, Frances H. Selig-son, Valeria Tarasuk, Susan Whiting, Bernadette M. Marriott (Consultant), Linda D. Meyers (Study Director). Preparation of the report was supported by the Food and Drug Administration of the U. S. Department of Health and Human Services, the Food Safety and Inspection Service of the U.S. Department of Agriculture, and Health Canada. Copies of the report are available from the National Academies Press (http://www.nap.edu). It is used here with permission, doi:10.1301/nr.2004.feb.73-79
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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 | 0.038 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.018 |
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".