Dietary supplementation of ferulic acid improves performance and alleviates oxidative stress of lambs in a cold environment
Bibliographic record
Abstract
Ferulic acid (FA) has been regarded as an antioxidant in domestic animals’ feed. This study was to investigate whether dietary FA supplementation could improve growth performance by decreasing oxidative stress of lambs in cold environment. Thirty-two 3-mo-old crossbred male lambs (Dorper × Small-tail Han sheep; 30.49 ± 0.46 kg) were randomly assigned into one of the following dietary treatments: CON (control, no FA), FA80 (80 mg FA kg−1 of diet), FA400 (400 mg FA kg−1 of diet), and FA2000 (2000 mg FA kg−1 of diet). Lambs fed the FA80 had higher (P < 0.05) average daily gain and lower (P < 0.05) feed efficiency than those fed CON and FA2000. The dry matter, organic matter, and neutral detergent fiber apparent digestibility was lower (P < 0.05) for FA2000 lambs than for lambs from other treatments. Serum total protein and albumin concentrations were greater (P < 0.05) for FA80 group than other groups. Lambs fed FA80 had higher (P < 0.05) plasma glutathione peroxidase and catalase activities and lower (P < 0.05) malondialdehyde (MDA) content than lambs fed CON. However, FA2000 group showed higher (P < 0.05) plasma MDA content than CON group. In conclusion, dietary supplementation of 80 mg FA kg−1 diet could improve growth performance by decreasing oxidative stress of lambs in cold environment.
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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 | 0.000 |
| 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".