Identifying Dietary Strategies to Improve Nutrient Adequacy among Ethiopian Infants and Young Children Using Linear Modelling
Bibliographic record
Abstract
Nutrient adequacy of young children's diet and best possible strategies to improve nutrient adequacy were assessed. Data from the Ethiopian National Food Consumption Survey were analysed using Optifood (software for linear programming) to identify nutrient gaps in diets for children (6-8, 9-11 and 12-23 months), and to formulate feasible Food-Based Dietary Recommendations (FBDRs) in four regions which differ in culture and food practices. Alternative interventions including a local complementary food, micronutrient powders (MNPs), Small quantity Lipid-based Nutrient Supplement (Sq-LNS) and combinations of these were modelled in combination with the formulated FBDRs to compare their relative contributions. Risk of inadequate and excess nutrient intakes was simulated using the Estimated Average Requirement cut-point method and the full probability approach. Optimized local diets did not provide adequate zinc in all regions and age groups, iron for infants <12 months of age in all regions, and calcium, niacin, thiamine, folate, vitamin B12 and B6 in some regions and age-groups. The set of regional FBDRs, considerably different for four regions, increased nutrient adequacy but some nutrients remained sub-optimal. Combination of regional FBDRs with daily MNP supplementation for 6-12 months of age and every other day for 12-23 months of age, closed the identified nutrient gaps without leading to a substantial increase in the risk of excess intakes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".