Bibliographic record
Abstract
ObjectivesAdequate zinc nutriture is critically important for human health, but the development of programmes to control zinc deficiency is limited by the lack of reliable information on population zinc status. The present analyses were conducted to: (1) estimate the absorbable zinc content of national food supplies; (2) compare this information with theoretical population requirements for zinc; and (3) use these results to predict national risks of inadequate zinc intake.Setting and design: National food balance data were obtained for 176 countries from the Food and Agriculture Organization of the United Nations. The amount of absorbable zinc in these foods was estimated from food composition data, and zinc absorption was predicted using a model developed by the International Zinc Nutrition Consultative Group (IZiNCG). Demographic data were obtained from United Nations estimates, and age- and sex-specific physiological requirements for absorbable zinc were estimated using IZiNCG recommendations.Results and conclusions: The mean per capita absorbable zinc content of national food supplies ranged from 2.98–3.01 mg day−1 in Western Europe and USA & Canada to 2.09 mg day−1 in Southeast Asia. The estimated percentage of individuals at risk of inadequate zinc intake ranged from 9.3–9.5% in the regions of North Africa & Eastern Mediterranean and USA & Canada to 33.1% in Southeast Asia. Overall, approximately 20.5% of the world's population is estimated to be at risk of inadequate zinc intake. Data on the absorbable zinc content of national food supplies can be used to determine whether further assessments of population zinc status and development of intervention programmes are warranted.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.644 | 0.373 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".