Net energy system of feed formulation with or without enzyme supplementation in growing-finishing pigs fed barley-based diets with alternative feed ingredients
Bibliographic record
Abstract
The aim of this study was to determine the growth performance and carcass characteristics of growing-finishing pigs fed diets formulated on net energy (NE) basis with or without exogenous enzyme supplementation. Twenty-four pigs with an initial body weight (BW) of 25 kg were randomly allotted one of three treatments; a barley-based control diet formulated on a digestible energy (DE) basis (Diet A), control diet formulated on an NE basis (Diet B), and Diet B + multicarbohydrase enzyme (Diet C). Pigs were offered their respective diets in a 3-phase feeding program. Individual pig BW and feed disappearance were monitored once every 2 wk. Pigs were slaughtered when they reached 100 kg BW to determine carcass characteristics. During phase 1, an improvement (P = 0.02) in feed intake was observed in pigs fed Diet C compared with Diet B. In phase 3, pigs fed Diet B showed improvement in daily gain (P = 0.02) and feed efficiency (P = 0.05) compared with Diet A. Overall, when compared with control diet, pigs fed Diet B showed significant improvement in daily gain (P = 0.05) and feed efficiency (P = 0.01). In conclusion, the results indicate a better growth performance with diets formulated using the NE system. Moreover, enzyme supplementation had no effect on the overall performance of 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) | 0.000 | 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 | 0.000 |
| 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".