Effect of dietary supplementation of diacylglycerol on growth performance, nutrient digestibility, and blood profiles in growing pigs fed corn–soybean-meal-based diet
Bibliographic record
Abstract
An experiment was conducted to study the effects of dietary supplementation of 1,2-diacylglycerol (1,2-DAG) and 1,3-diacylglycerol (1,3-DAG) as emulsifier in a corn–soybean-meal-based diet on growth performance, nutrient digestibility, and blood characteristics in growing pigs. A total of 75 cross-bred [(Landrace × Yorkshire) × Duroc] growing pigs with an initial body weight (BW) of 24.27 ± 1.58 kg were used in a 6 wk trial. Pigs were randomly allocated into one of three treatments according to their sex and BW (five replicates with two gilts and three barrows per replication pen). Treatments were as follows: (1) CON, basal diet; (2) TRT1, CON + 0.1% 1,2-DAG; and (3) TRT2, CON + 0.1% 1,3-DAG. The DAG diets trended to increase BW, average daily gain (ADG), and average daily feed intake (P < 0.10) compared with CON diet in growing pigs. Pigs fed with DAG diets had higher (P < 0.05) digestibility of dry matter and nitrogen compared with the CON diet. Dietary supplementation with 1,3-DAG diet increased (P < 0.05) high-density lipoprotein cholesterol concentration, and triglycerides concentration was decreased (P < 0.05) in the 1,2-DAG and 1,3-DAG diets. In conclusion, dietary DAG supplementation positively affected ADG, nutrient digestibility, and blood profiles in growing pigs, especially 1,3-DAG.
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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".