Fortification of dairy milk with docosahexaenoic acid (DHA) through feed supplementation of dairy cattle feed - a new horizon in dairy industry.
Bibliographic record
Abstract
Docosahexaenoic acid (DHA) is an omega-3 fatty acid essential for structural development of the brain and eyes in the infants and maintenance of normal vision and neural functions in adults. DHA is also vital for the integrity of heart and vascular system, and is implicated in relieving inflammatory conditions and arthritis pain and in preventing cancer. Human body cannot synthesize DHA sufficiently, and most common source of DHA is marine food. Lowest breast-milk DHA values (0.06–0.14%) in nursing mothers found in Pakistan and inland areas of Canada have been attributed to a lack of dietary intake of marine food. In Canada, a novel approach of fortifying the dairy milk by supplementing the cattle feed with DHA-rich herring meal has been used to enhance the dietary intake of DHA in the country. In Pakistan a homegrown source of DHA is needed for supplementing cattle feed is needed to augment the low availability of marine food sources, and seems feasible to cultivate the marine red alga Crypthecodinium cohnii, which is a prolific producer of DHA and has been used as a non toxic pharmaceutical supplement. Crypthecodinium cohnii addition to duck feed has been shown to result in a significant increase in the DHA content of the fed animal. The current study deals with developing a sustainable mass culturing system for C. cohnii in Pakistan, using both bioreactors and open pond fields. The fortification of cattle feed is intended for both large scientifically managed “smart” dairy farms, and small to medium size family owned farms in different parts of the country. Our research is focused on developing protocols for custom designing the DHA enrichment of cattle feeds used in the area.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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".