Cost-effective options for increasing consumption of under-consumed food groups and nutrients in the USA
Bibliographic record
Abstract
OBJECTIVE: To identify the most cost-effective options/contributors of under-consumed food groups and nutrients in the USA. DESIGN: Twenty-four-hour dietary recall data were used for the dietary sources of under-consumed food groups and nutrients. Costs were estimated using USDA National Food Price Database 2001-2004 after adjustments for inflation using Consumer Price Index. SETTING: National Health and Nutrition Examination Survey, 2013-2016. PARTICIPANTS: A total of 10 112 adults aged 19+ years. RESULTS: Top five cost-effective options for food groups were apple and citrus juice, bananas, apples, and melons for fruit; baked/boiled white potatoes, mixtures of mashed potatoes, lettuce, carrots and string beans for vegetables; oatmeal, popcorn, rice, yeast breads and pasta/noodles/cooked grains for whole grain; and reduced-fat, low-fat milk, flavoured milk and cheese for dairy. Top five cost-effective sources of under-consumed nutrients were rice, tortillas, pasta/noodles/cooked grains, rolls and buns, and peanut butter-jelly sandwiches for Mg; grits/cooked cereals, low- and high-sugar ready-to-eat (RTE) cereal, rolls and buns, and rice for Fe; low- and high-sugar RTE cereals, rice, protein and nutritional powders, and rolls and buns for Zn; carrots, margarine, other red and orange vegetables, liver and organ meats, butter and animal fats for vitamin A; and citrus juice, other fruit juice, vegetable juice, mustard and other condiments, and apple juice for vitamin C. CONCLUSIONS: Apple/citrus juice, white potatoes/carrots, oatmeal, RTE cereals and milk were the most cost-effective food sources of multiple under-consumed food groups and nutrients and can help promote healthy eating habits at minimal cost.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".