DUAL FORTIFICATION OF COMMON SALT – TECHNOLOGICAL HURDLES AND WAY AHEAD
Bibliographic record
Abstract
disorders (IDD) and vitamin A deficiency have been identified as the major public health problems in our country1. Fortification of common salt with iron has been developed by the National Institute of Nutrition (NIN) as a public health strategy for the control of IDA on the lines of iodization of salt for the effective control of IDD2. However, with the advent of universal iodization of edible salt as a National policy in 1988, NIN undertook research studies aimed at development and testing of double fortified salt (DFS) containing iodine and iron for reducing the deficiencies of both these micronutrients3. In view of their antagonistic chemical properties, the incorporation of iron and iodine in salt requires a stabilizer. NIN developed a DFS formulation using sodium hexametaphosphate (SHMP) as a stabilizer. SHMP is intended to protect iodine and prevent the interaction between the iron and iodine and also with the other constituents of the salt. The stability, bioavailability and acceptability of DFS were determined and found to be good3. The Micronutrient Initiative (MI) in Canada4 and a company in Chennai with the trade name of the salt “Nutrisalt”5 have developed two other formulations of DFS, in which physical
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".