Effects of Supplementation of Branches and Leaves Trimmed from Tea Plant on Growth Performance, Rumen Fermentation and Meat Composition of Nanjiang Yellow Goats
Bibliographic record
Abstract
Thirty-two 6-month-old, healthy Nanjiang Yellow goats (34.6 ± 4.16 kg) were randomly divided into four treatments to evaluate the supplementary effects of branches and leaves trimmed from tea tree (BLTT) on growth performance, rumen fermentation characteristics, and meat composition in fattening goats. The control goats were fed a basal diet. Treatments 1, 2 and 3 were fed 2%, 4% and 6% of BLTT respectively. After a 60 d feeding trial, blood samples were collected for the analysis of the antioxidant profile and goats were slaughtered to obtain the rumen fluid and carcass samples for determination of rumen fermentation characteristics and meat composition perameters. Increased final body weight was observed in goats fed 4% BLTT compared with control. The activity of superoxide dismutase was increased in the 4% BLTT-treated group, while glutathione peroxidase and malondialdehyde followed the quadratic curve. Quadratic curves were also observed for villus height, crypt depth and the ratio of villus height to crypt depth in the jejunum. The quadratic effect was obtained for total essential amino acids, and individual amino acids threonine and leucine. The saturated fatty acid C16:0 was decreased with 4% of BLTT supplementation, and a quadratic effect was observed for polyunsaturated fatty acid C18:3 (n-3). To sum up, our findings revealed that BLTT supplementation has a positive effect on body antioxidative status, gastrointestinal development, rumen fermentation characteristics and overall growth performance and meat quality in goats.
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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".