Effect of Dietary Organic Selenium Supplementation On Mass And Enzyme Activities Of Rumen Microbes Of Goat
Bibliographic record
Abstract
Eighteen cross-bred goats, 3–4 months old, weighing up to 10.5 kg and appearing to be in good condition, were randomly divided into three groups: LC, HC, and HC+Sey, with six goats in each group. The LC group received only a basal diet, while the HC group was provided a high concentrate diet enriched with organic selenium (Se) at 0.3 mg per kg in food, mixed into the morning feeding concentrate. All animals were given guar hay as the basal diet, and 150 g of concentrate was fed twice daily at 0800 and 1700 hours. Statistical analysis was conducted using one-way ANOVA, with significance set at P < 0.05. Results indicated that goats in the HC and HC-SeY groups had significantly higher (P < 0.05) molar concentrations of short-chain fatty acids (SCFA), such as acetic acid, propionate, and butyrate, in their rumen fluid compared to those in the LC group. The pH of the rumen fluid also significantly dropped in the HC and HC-SeY groups (P < 0.05), although no significant differences were observed between the two. Goats on the HC and HC-SeY diets also had a substantially higher protozoan and bacterial count (P < 0.05) compared to the LC group, with no significant differences between HC and HC-SeY groups. Enzyme activity in the rumen fluid was significantly higher (P < 0.05) in the HC and HC-SeY groups compared to LC, suggesting that high concentrate and selenium yeast diets promote a more robust microbial community in goats than a low concentrate diet alone.
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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".