Growth performance, carcass traits, and histological changes of goats supplemented with different sources of phytochemicals
Bibliographic record
Abstract
Phytogenic feed additives are increasingly used to improve animal health and productivity. This study compared the effect of supplementation with tannin to an herbal mixture consisting of ginger, garlic, artemisia, and turmeric on the performance, intestinal parasites, blood metabolites, carcass characteristics, and histology of muscles and intestine of goats. Twenty-seven Shami male goats were assigned to three treatments (n = 9): non-supplemented goats fed a control diet (CC); goats supplemented with 10g /animal/day of quebracho tannins as a source of condensed tannin (TT); and goats supplemented with 10g/animal/day of an herbal mixture (HM). All the animals received a basal diet consisted of concentrate feed mixture and alfalfa hay. The supplementation improved growth performance, nutrients digestibility, and serum immunoglobulins concentration (P < 0.05). The supplementation decreased fecal parasite counts, blood cholesterol, and glutamic-pyruvic transaminase (GPT) enzyme and improved blood glucose (P < 0.05). The supplementation decreased renal and meat fat, and group HM revealed higher polyunsaturated fatty acids and α-Linolenic acid in meat (P < 0.05). Tannin supplementation (TT group) negatively affected the histology of muscles and intestines. The results provide evidence for the beneficial use of an herbal mixture in the diet to improve animal performance, health status, 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) | 0.000 | 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".