Effect of different dietary levels of low-glucosinolate rapeseed (canola) meal and non-starch polysaccharide-degrading enzymes on growth performance and gut physiology of growing turkeys
Bibliographic record
Abstract
Zduńczyk, Z., Jankowski, J., Juśkiewicz, J., Mikulski, D. and Slominski, B. A. 2013. Effect of different dietary levels of low-glucosinolate rapeseed (canola) meal and non-starch polysaccharide-degrading enzymes on growth performance and gut physiology of growing turkeys. Can. J. Anim. Sci. 93: 353–362. One-day-old male turkey poults were randomly assigned to eight dietary treatments and were fed diets containing 0, 60, 120, or 180 g kg−1of low-glucosinolate rapeseed meal (RSM) from 1 to 56 d of age without or with a commercial non-starch polysaccharide-degrading enzyme product containing pectinase, cellulase, xylanase, glucanase, mannanase and galactanase activities. There were no significant differences (P>0.05) in body weight and feed conversion ratio between the Control soybean meal-based diet and the diets containing RSM. Overall, the inclusion of up to 180 g kg−1of RSM did not affect the growth performance of turkeys. When compared with the Control treatment, the diet with the lowest RSM content increased the pH of the small intestinal contents and the amounts of cecal digesta. The moderate level of RSM also increased the pH of the small intestinal contents although no increase in the cecal digesta contents was noted. In comparison with other treatments, the highest RSM content increased the content of the small intestinal digesta considerably and decreased the pH of the small intestinal contents to that of the Control treatment. All three dietary levels of RSM significantly reduced the activity of bacterial β-glucuronidase in the ceca. Enzyme supplementation tended to reduce ileal viscosity (P=0.079), decreased ammonia concentration, increased the glycolytic activities of the intestinal microflora enzymes α-glucosidase, α-galactosidase, and β-galactosidase, decreased the activity of β-glucuronidase and increased the growth rate of turkeys.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".