Length of concentrate finishing affects the fatty acid composition of grass-fed and genetically lean beef: an emphasis on trans-18:1 and conjugated linoleic acid profiles
Bibliographic record
Abstract
Intensively finishing cattle on a high-grain diet is generally used to enhance marbling, whereas extensively finishing on grass is known to provide improved muscle fatty acid profiles. The objective of this study was to evaluate to what extent intensive concentrate finishing (0, 1 or 2 months) can be combined with forage feeding without negatively affecting the fatty acid profile of genetically lean animals. Bulls from the 'Asturiana de los Valles' breed were reared under grazing conditions with/without final finishing on a barley-based concentrate: 0 months (control; n=7), 1 month (n=10) and 2 months (n=7). Yearling bulls were slaughtered commercially at an average live weight of 516±9.8 kg. Increasing the finishing time on concentrate significantly increased the saturated and monounsaturated fatty acids, whereas polyunsaturated fatty acids (PUFAs) tended to decrease and it was not possible to increase the long-chain PUFA content in muscle tissue of this breed. An increase was observed for total trans-18:1 (average 5.5% with grain v. 3.7% for grass). The 11t-18:1/10t-18:1 ratio was significantly higher in grass-fed (average 8.1) compared with grain-finished animals (average 1.1). Grass or limited concentrate finishing reduced the n-6/n-3 ratio in muscle tissue (average 3.6 for 0 and 1 month, and 4.9 for 2 months on grain finishing). The beef was within or close to the recommended values for human consumption (i.e. polyunsaturated/saturated>0.45, n-6/n-3<4.0), and total trans-FA content was low. However, finishing increased the content of undesirable trans-18:1 and conjugated linoleic acid isomers, particularly after 2 months, whereas grass finishing was judged to provide a healthier beef fatty acid profile.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".