Bibliographic record
Abstract
In recent years, increasing attention has been drawn to the relatively slow pace of progress in intervention efforts against micronutrient deficiencies in sub-Saharan Africa. Recent data indicate that the problem of micronutrient deficiencies remains severe, a situation compounded by inadequacy of institutional capacities and resources required for implementing well-defined control strategies. Supplementation programmes started in earnest after the 1992 International Conference on Nutrition. More recently, there has been an increase in the rate of coverage of vitamin A supplementation of children under five years of age, attributed to the integration of vitamin A capsule distribution into national immunization days. However, a major constraint to vitamin A capsule delivery is a poorly functioning health infrastructure. Transportation facilities are poor, and there are shortages of equipment and trained personnel. Food fortification, which was initially not considered a front-line approach because of the lack of infrastructural facilities in most countries, is currently being pursued with new ideas for adapting existing technologies to local resources and needs. Early attempts at developing food-based strategies involved promoting the production and consumption of vitamin A– rich foods as well as encouraging small-scale animal production. A major shortcoming of these earlier food-based interventions is a lack of quantifiable and convincing data demonstrating impact. There is a growing movement to involve women in intervention programmes. Africa is primarily agrarian. Micronutrient intervention efforts have not fully exploited the untapped potential of the existing food systems and the immense human resources of the agricultural sector.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.003 |
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".