Bibliographic record
Abstract
Evidence for the effectiveness of the enrichment of food products with n-3 fatty acids by inclusion of either plant- or fish-derived materials in the diets of chickens, turkeys, ostriches, cows, pigs, and goats has been reviewed. Both linseed oil/meal and fish products can increase the levels of total n-3 fatty acids in animal products, including milk, eggs, meat, and deli products. The extent of this increase in n-3 fatty acid contents seems to be dependent on the nature of diet supplementation. Encapsulation of linseed oil may result in higher milk cow ALA contents, as compared to unprotected linseed oil. Available literature indicates that the levels of EPA and DHA in food products may be increased more, if the animals' diet was supplemented with fish products rather than linseed products. However, organoleptic properties of food products may be compromised. This pitfall may be reduced by the addition of antioxidants and/or application of micro-encapsulation. Generation of transgenic animals and plants has shown very promising results. Thus far, transgenic pigs and mice have been successfully generated. These animals have a low ratio of n-6:n-3 fatty acids in their tissues and milk. The advantages and disadvantages of the above-mentioned methods have been discussed. The evidence for health-promoting effects of such enriched food products has been included.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".