Evaluation of emulsified-balanced oil in lactating sows
Bibliographic record
Abstract
To evaluate an emulsified-balanced oil (EBO) replacement of soybean oil in different ratios on lactating sows and piglets. Forty sows at day 107 of gestation were divided into four treatments: CON, the basal diet without EBO; T1, EBO replaced 50% soybean oil; T2, EBO replaced 100% soybean oil; T3, EBO used two times of soybean oil. The experiment continued until weaning on day 21 of lactation, lasting for 28 days. T1 and T3 increased ( p < 0.05) healthy litter size. The digestibility of ether extract (T3) and crude protein (T1 and T3) increased ( p < 0.05). EBO increased ( p < 0.05) serum alkaline phosphatase, urea, superoxide dismutase, catalase, tumor necrosis factor-α, interleukin-8, and interleukin-6 and decreased ( p < 0.05) the serum immunoglobulin A in sows. Higher ( p < 0.05) milk fat in colostrum, higher ( p < 0.05) milk protein, and lower ( p < 0.01) lactose in milk were observed with EBO supplementation. EBO also increased ( p < 0.05) the contents of total protein (TP), glucose (GLU), cholesterol (CHOL), and calcium (Ca) in the serum of piglets. A higher ( p < 0.05) relative abundance of microbial composition in piglet feces was also observed. In conclusion, EBO improved reproductive performance and nutrient digestibility, regulated blood biochemical indexes and milk composition in sows, and improved blood biochemical indexes and fecal microbial composition in piglets.
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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".