Folic acid fortification of double fortified salt
Bibliographic record
Abstract
Abstract The addition of folic acid to Double Fortified Salt (with iron and iodine) aims to simultaneously ameliorate three major micronutrient deficiencies in vulnerable populations. To make Triple Fortified Salt, we added folic acid to the iodine solution (first method) and the iron premix (second method) that are used to fortify salt with iron and iodine. When added through the solution, sodium carbonate was needed to dissolve folic acid and to adjust pH. Alternately, folic acid was added either to the iron core or sandwiched between the core and TiO 2 layer of the iron premix. Folic acid and iodine were stable in all cases, retaining more than 70% of the added micronutrients after six months at 45 °C/60–70% relative hu. Adding folic acid to the premix's iron core is preferred as folic acid retention was slightly higher, and the added folic acid did not impact the salt's colour. The additional cost for adding the micronutrients to salt is about 27¢/person per year. Folic acid in the fortified salt made with the preferred method was stable in cooking and did not affect selected cooked foods' sensory properties. The technology is a cost-effective approach for simultaneously combating iron, iodine, and folic acid deficiencies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".