Food Science Challenge: Translating the Dietary Guidelines for Americans to Bring About Real Behavior Change
Bibliographic record
Abstract
Food scientists and nutrition scientists (dietitians and nutrition communicators) are tasked with creating strategies to more closely align the American food supply and the public's diet with the Dietary Guidelines for Americans (DGA). This paper is the result of 2 expert dialogues to address this mandate, which were held in Chicago, Illinois, and Washington, D.C., in early October 2010 between these 2 key scientific audiences. It is an objective that has largely eluded public health experts over the past several decades. This document takes the perspective of food scientists who are tasked with making positive modifications to the food supply, both in innovating and reformulating food products, to respond to both the DGA recommendations, and to consumer desires, needs, and choices. The paper is one of two to emerge from those October 2010 discussions; the other article focuses on the work of dietitians and nutrition communicators in effecting positive dietary change.
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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 | 0.051 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.023 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".