Dehulled small millets: The promising nutricereals for improving the nutrition of children
Bibliographic record
Abstract
Good nutrition during a child's early years lays a strong foundation of health for the rest of its life. Yet in India, there is widespread prevalence of undernourishment among children below 5 years of age. Within the Indian context, small millets have great potential as a healthy food to address this challenge by the virtue of their nutritional qualities. However, there are many problems with the current processing technology for small millets, whereas the use of value-added products was minimal. To address this, an assessment of existing small millet processing machinery was undertaken, and a double chamber centrifugal dehuller was developed, which had higher recovery of dehulled unpolished millets and met requirements at the village and enterprise levels. To demonstrate the health benefits of consuming value-added small millets, a study of supplementation of multi-millet health mix on the nutritional status of primary schoolchildren was conducted in Thondamuthur Block of Coimbatore District, India. Multi-millet health mix was formulated from kodo millet, little millet, foxtail millet, finger millet, and wheat with the inclusion of pulses. It contained 65.45-g carbohydrate, 11.46-g protein, 4.94-g fat, 4.94-g fibre, 4.07-mg iron, 112-mg calcium, 268.52-mg phosphorus, and 349 calories of energy per 100 g. The study indicated that there was a significant increase in height, weight, and haemoglobin level of the schoolchildren who regularly consumed the formulated multi-millet health mix. The improved huller and value-added food product developed can be feasible options for improving nutrition security and livelihoods through increased use of small millets.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".