Estimating the potential of spices for mineral provision in a refugee context in East Africa
Bibliographic record
Abstract
Abstract Micronutrient deficiency remains an enormous problem in refugee settings. Transforming refugees’ food systems through the scaling up of kitchen gardening and fortifying relief food crops with minor food components including nutrient-dense spices can help improve the quality of staple foods. Globally, spices are indispensable in the daily diet and play an important role in the socio-cultural setting of different communities. Forty turmeric and curry powder samples were collected from different market establishments and geographic locations in East Africa. The samples were analyzed for selected elements using Portable X-Ray Fluorescence (PXRF). The contents of potassium (K), calcium (Ca), zinc (Zn) and strontium (Sr) in turmeric powder were statistically different among geographic origins (Ethiopia, Kenya and Uganda). We also aimed to determine if a small portion of spices (turmeric (5 g) and curry (4 g)) would contribute to an adequate intake (AI) or recommended dietary allowance (RDA) for selected minerals, for refugee men and women aged between 19 and 50 years as defined by the Food and Nutrition Board of the Institute of Medicine (IOM). For the reference groups, the contributions of turmeric and curry powder to AI/RDA for K, Ca and Zn varied between 0.48 to 4.13%. On the other hand, turmeric was identified to contribute > 20% AI/RDA for refugee men and women aged between 19 and 50 years for two micro minerals: manganese (Mn) and iron (Fe). Considering turmeric and curry powder of East African origins are good sources of minerals and present acceptable toxic metal(loid)s loads coupled with low cost, these spices particularly turmeric should be more widely popularized and recommended for food-to-spice fortification among the refugee population located in East Africa.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".