Could local foods achieve recommended calcium intakes for nutritionally vulnerable populations in Uganda, Guatemala, and Bangladesh?
Bibliographic record
Abstract
Globally, dietary intake of calcium is often insufficient, and it is unclear if adequacy could be achieved by promoting calcium-rich local foods. This study used linear programming and household consumption data from Uganda, Bangladesh, and Guatemala to assess whether local foods could meet calcium population reference intakes (Ca PRIs). The most promising food-based approaches to promote dietary calcium adequacy were identified for 12- to 23-month-old breastfed children, 4- to 6-year-old children, 10- to 14-year-old girls, and nonpregnant and nonbreastfeeding (NPNB) women of reproductive age living in two regions of each country. Calcium-optimized diets achieved 75-253% of the Ca PRI, depending on the population, and were <100% for 4- to 6-year-olds in one region of each country and 10- to 14-year-old girls in Sylhet, Bangladesh. The best food sources of calcium were green leafy vegetables and milk, across geographic locations, and species of small fish, nixtamalized (lime-treated) maize products, sesame seeds, and bean varieties, where consumed. Food-based recommendations (FBRs) achieving the minimum calcium threshold were identified for 12- to 23-month-olds and NPNB women across geographic locations, and for 4- to 6-year-olds and 10-to 14-year-old girls in Uganda. However, for 4- to 6-year-olds and 10- to 14-year-old girls in Bangladesh and Guatemala, calcium-adequate FBRs could not be identified, indicating a need for alternative calcium sources or increased access to and consumption of local calcium-rich foods.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".