An Investigation of the Stability of Double Fortified Salt during Storage and Distribution in Nigeria
Bibliographic record
Abstract
Double fortified salt prepared with various formulations of iodine and microencapsulated iron has been investigated for stability in a five-month period, under actual field conditions of storage and distribution, in the south coastal and north highland regions of Nigeria. A control of iodated salt and fifteen 500g sample packets of double fortified salt, prepared in duplicate, were sealed in polyethylene bags. These samples, and a datalogger for monitoring temperature and humidity, were packaged into a typical bundle which was overwrapped with polyethylene film and inserted into the distribution network starting from a salt manufacturer's facility to the consumer. Iodine retention values of up to 79% were obtained when iodated salt was used and 64% when potassium iodide premix was used. Less than 20% of the ferrous iron was oxidized to ferric iron, and about 25% of the iron in formulations with sodium ferric EDTA (NaFeEDTA) reduced to ferrous iron. Double fortified salt with ferrous fumarate was generally more stable than those prepared with the other iron premixes. The polyethylene film overwrap of salt packs in the bundles provided a significant protection from ambient humidity. Iodine and the ferrous iron were protected in salt, double fortified with iodine and microencapsulated iron, during distribution and retail in typical tropical conditions in Nigeria's coastal and northern highlands.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".