CHIA SEEDS: A PROMISING SUPPLEMENTATION TO CURB MALNUTRITION IN PAKISTAN
Bibliographic record
Abstract
Now-a-days, chia seeds; the sleeping beauty of Mexico, have become center of focus among scientist, food technologist and nutritionist owing to its nutritional composition in mitigating challenges to malnutrition. Globally, malnutrition; expressed in terms of stunning, wasting and obesity, has reached at a prevalence level of 21.9%, 7.3% and 5.9% respectively. Pakistan, the 7th most populous country, has also been severely affected by malnutrition indicating stunning, wasting, over-weight and under-weight around 37.6%, 7.1%, 2.5% and 23.1% respectively. the chemical composition of chia seeds indicates 30-40% oil mostly PUFA, 26-41% carbohydrates, 16-20% protein mostly prolamins, glutelins, globulins and albumins and 23-41% fiber totally equivalent to RDA for adults. Besides these basic nutrients chia seeds are rich source of B-vitamins and minerals particularly Ca, P and K even more than milk. The antioxidant profile which indicates the presence of polyphenols particularly gallic, caffeic, chlorogenic, ferulic, and rosmarinic acids potential has also been proved it to be significant against degenerative diseases such as arthritis, diabetes, cancer, and cardiovascular diseases. These statistical figures together with nutritional profile have led the development of food products with improved nutritional profile without compromising consumer requirements. Many researches have been conducted over the use of chia seeds in development of food products belonging to baking, dairy, extrusion, meat and nutraceuticals and found chia incorporation successful. Whilst the novelty of chia seeds has been acknowledged by EU legislation, the safety limits have also been laid out since the excess of every thing is bad. According to EU legislation, chia seeds can be consumed raw not more than 15g/day while it can have incorporated in processed foods not more than 10%.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".