Dietary Sugar Beet Tops and Prebiotic Effect on Nutrient Digestibility, Caecal Activity and Organ Histology of Weaning Rabbits
Bibliographic record
Abstract
The current study was designed to evaluate the effect feeding weaning rabbits sugar beet tops (SBT) with prebiotic on digestibility of nutrients and caecal microbial activity, volatile fatty acids estimation and organs histology. Fifty four 6-week-old unsexed weaning NZW rabbits were randomly distributed in a factorial (3 × 2) design to six equal groups. Sugar beet tops (SBT) was used at levels 0.0, 20 or 30% of the diet instead of the percent of alfalfa hay in the control diet in presence of 1.0 g/kg diet or absence of prebiotic (Perfect). Digestibility coefficients of dry matter (DM), crude protein (CP), ether extract (EE) and NFE of 12-week old NZW rabbits were improved by feeding SBT-containing diets. Dietary SBT level had no significant effect on pH value, TVFA,s and NH3-N values for caecum content of 12 weeks old NZW rabbits, however, total bacteria, E. coli and lactobacillus counts were significantly affected. Feeding SBT-containing diets did not alter plasma concentrations of AST and ALT of 12-week-old rabbits. Dietary prebiotic had a positive effect on the digestibility coefficients of DM, OM, CP, EE, CF and NFE of growing rabbits. Added prebiotic had a positive effect on caecal fermentative activities and caecal microbial activity of growing rabbits. The pH value, ammonia-N level, total bacteria and lactobacillus counts of rabbits fed prebiotic-supplemented diets were significantly higher but E. coli count was reduced as compared to control ones. This study concluded that sugar beet tops can be safely used in rabbit diets up to 30% instead of alfalfa hay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".