Influence of dietary protease supplementation to high- and low-protein diets on growth performance, nutrient digestibility, blood profiles, and gas emissions in finishing pigs
Bibliographic record
Abstract
A total of 160 growing pigs (Duroc × (Landrace × Yorkshire); 54.11 ± 2.81 kg)) were randomly assigned to 1 of 4 treatments in a 2 × 2 factorial design with two different levels of crude protein (CP) (15% or 13%) with or without 0.025% protease for 10 weeks. Each treatment had eight replicates with five pigs (three gilts and two barrows) per pen. During the overall experimental period, pigs fed a high CP diet supplemented with 0.025% protease showed significantly higher ( P < 0.05) daily gain and reduced gain-to-feed ratio compared to those fed low CP diet without protease. Moreover, at the end of week 10, pigs in the protease-treated group exhibited significantly increased ( P < 0.05) BW, apparent total tract digestibility of nitrogen and energy, and significantly reduced ammonia emission. Furthermore, glucose, blood urea nitrogen, and creatinine concentrations were significantly increased ( P < 0.05) in pigs fed a high CP diet supplemented with protease. These findings suggest that incorporating 0.025% protease into high CP diets may serve as an effective strategy to enhance the growth and overall performance of pigs while potentially mitigating environmental nitrogen emissions.
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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 | 0.001 |
| 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 | 0.001 |
| 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".