Use of national food balance data to estimate the adequacy of zinc in national food supplies: methodology and regional estimates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: Adequate 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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it