Dietary manipulation to increase conjugated linoleic acids and other desirable fatty acids in beef: A review
Bibliographic record
Abstract
The diet is the source of many essential fatty acids such as linoleic and linolenic acids for all mammals. These fatty acids either, as altered isomers or as other elongated products, have been found to provide unique advantages to human health. Currently two conjugated linoleic acids (CLA) isomers (cis-9, trans-11 C18:2; trans-10, cis-12 C18:2) and two elongated products of linolenic acid [eicosapentaenoic acid (EPA, C20:5 n-3), docosahexaenoic acid (DHA, C22:6 n-3)] have been recognized for their roles in maintaining human health. Consumers can obtain these functional fatty acids from beef if the feeding management of beef cattle can be altered to include precursor fatty acids. Diet, breed, and gender are important factors that affect total fat content and/or the fatty acid profile of beef with regard to CLA, EPA, and DHA. Diet provides the precursor fatty acids that are altered and deposited, and breed dictates, the amount of fat that is deposited. These fatty acids can be increased in beef by increasing the forage:concentrate ratio, inclusion of non-fermented forage, and supplementation with various oils or oil seeds. The CLA and vaccenic acid (trans-11 C18:1) concentration in beef was increased by feeding sunflower oil or seeds, linseed, and soybean oil supplemented diets, while cattle fed linseed and fish oil supplemented diets had increased concentrations of EPA and DHA. Although the concentration of these fatty acids can be increased in beef, there is a need to further the understanding of the mechanism by which they exert positive affects on human health. Key words: Cattle, beef, fatty acids, conjugated linoleic acid, eicosapentaenoic acid, docosahexaenoic acid
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".