Growth Performance, Cecal Fermentation and Blood Biochemistry of Rabbits Fed Diet Supplemented with Urea - Bentonite Combination
Bibliographic record
Abstract
Seventy six New Zealand white rabbits of about 700 ± 80 gm live weight were used to evaluate the influence of diet supplemented with urea-bentonite combination on cecal characteristics, metablism and rabbit growth performance. The rabbits were assigned into four dietary treatments control, bentonite-supplemented (with 2.5% sodium bentonite), urea-supplemented (with 1% urea) and urea – bentonite co-supplemented (1% urea+ 2.5% sodium bentonite). Co-supplementation decreased cecal pH value and ammonia nitrogen (NH3-N) concentration, increased total volatile fatty acids (VFAs) concentration, increased butyrate at the expence of acetate and propionate, increased fermentation efficieny and bacterial mass. Additionally, co-supplementation increased serum total proteins and glucose concentrations while no change was recorded in serum urea and creainine concentrations. Serum levels of AST were decreased in all experimental groups in comparison with control group whereas, serum levels of ALT were increased only by addition of urea alone. Moreover, urea-bentonite combination achieved an improvement in live weight gain and feed conversion effeciency by about 32% and 33% respectively with reference to control and thus considered cost effective. The study indicates that, a low cost urea-bentonite combination can replace about 50% of protein content of the high cost conventional rabbit feed and additionally improve rabbit growth performance that would be a valuable feeding strategy in developing countries.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".