Evaluation of phytase and β-mannanase on growth performance, nutrient utilization, fecal condition, and back fat thickness in growing and finishing pigs
Bibliographic record
Abstract
This study examined the impact of phytase and β-mannanase supplementation on growth performance, nutrient utilization, fecal condition, and backfat thickness in growing and finishing pigs. In the first experiment, 64 growing pigs (average body weight 24.3 ± 3.51 kg) were divided into two groups with eight replications (four pigs per replicate): one receiving a basal diet and the other diet supplemented with 0.02% phytase and 0.05% β-mannanase. The supplemented group showed significant improvements in average daily gain and nutrient digestibility for energy, nitrogen, and dry matter ( p < 0.05), without changes in feed intake or fecal score ( p > 0.05). In the second experiment, 56 finishing pigs (average body weight 54.36 ± 3.53 kg) were also split into two groups with seven replications (four pigs per replicate): one on a basal diet and the other on a diet with 0.04% β-mannanase. While β-mannanase supplementation did not significantly affect growth performance ( p > 0.05), it did enhance energy digestibility by the study’s end ( p < 0.05). No significant effects were found on backfat thickness, lean meat percentage, or fecal score ( p > 0.05). The study concluded that phytase and β-mannanase together enhance growth and nutrient digestibility in growing pigs; β-mannanase alone boosts nutrient utilization in finishing pigs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".