PSV-2 Effects of protease on growth performance, fecal gas emission of 25- to 55-kg pigs fed low or high density diets
Bibliographic record
Abstract
Abstract Volatility in feed ingredient prices prompts animal nutritionists to evaluate alternative methods to control feed costs. The objective of this study was to evaluate the effects of protease in growing pigs fed either high or low-density diets. A total of 140 pigs [(Landrace×Yorkshire)×Duroc] were used in a 6-wk study with an initial BW of 24.1 ± 0.02 kg equally distributed in 7 pens per treatment fed one of the following treatments: High-density diet with 3400 kcal ME/kg, 19.5% CP, and 0.85% SID Lys; High-density diet + 125 g/t Jefo Protease (Jefo, Canada); Low-density diet with 3300 kcal ME/kg, 17.6% CP, and 0.83% SID Lys; and Low-density diet + 125 g/t Jefo Protease. Diets were corn, soybean meal-based with 12% rice bran and 8% wheat bran. Data were subjected to statistical analyses as a completely randomized design using a 2 × 2 factorial arrangement with pen as the experimental unit. Differences among treatment means were determined using Duncan’s multiple range test with level of significance at P ≤ 0.05. High-density diets (P = 0.01) and protease supplementation (P = 0.05) significantly improved G:F in pigs (Table 1) compared to low-density diets and no protease supplementation. NH3 and H2S gas emission tended to be lower (P ≤ 0.10) in diets supplemented with protease. There were no statistical differences (P > 0.10) in initial weight, final weight, ADG, and ADFI. In conclusion, protease supplementation and high density diets improved G:F in 25- to 55-kg 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".