Effects of dietary supplementation of diacylglycerol on growth performance, nutrient digestibility, fecal noxious gas emission, and hematology parameters in weaned piglets
Bibliographic record
Abstract
A total of 120 21-day-old weaned piglets ((Yorkshire × Landrace) × Duroc) were used to evaluate the effects of dietary supplementation of diacylglycerol (DAG) on the growth performance, nutrient digestibility, fecal noxious gas emission, and hematology parameters in a 42-day experiment. All pigs were randomly assigned to three groups based on the initial body weight (6.47 ± 0.45 kg). There were eight replicate pens per treatment and five pigs (three barrows and two gilts) per pen. The DAG in the levels of 0%, 0.05%, or 0.10% was used as supplement to the corn–soybean meal-based basal diet. Piglets fed the diet supplemented with graded levels of DAG linearly increased the average daily gain (ADG) during days 22–42 ( P = 0.027) and 1–42 ( P = 0.048). In addition, a tendency in the linear reduction of fecal ammonia ( P = 0.095), hydrogen sulfide ( P = 0.078), and methyl mercaptan ( P = 0.085) emission was observed by increasing the DAG levels in the diet. However, feeding pigs with DAG-containing diet had no significant effects on the nutrient digestibility and hematology parameters. Therefore, the suitable dose of DAG used in the diet of weaned piglets was at 0.10% to improve ADG and reduce fecal gas emission.
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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) | 0.001 | 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 | 0.001 | 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".