Effects of dietary levan-type fructan on growth performance, blood profiles, fecal noxious gas emissions, and litter performance in lactating sows
Bibliographic record
Abstract
In total, 28 multiparous sows (n = 7) and their litters were used to determine the effects of levan-type fructan supplementation on growth performance, nutrient digestibility, blood profiles, and fecal noxious gas emissions. The dietary treatments included (1) CON, basal diet; (2) FC1, CON + 0.1% fructan; (3) FC2, CON + 0.2% fructan; and (4) FC3, CON + 0.3% fructan. No significant differences were observed (P > 0.05) in body weight (BW), average daily feed intake, or back fat loss in lactating sows among treatments. However, blood lymphocyte concentrates were higher (P < 0.05) in sows fed levan-containing diets than in those fed the CON diet. Weaning BW and overall average daily gain (ADG) of piglets in the FC3 treatment were higher (P < 0.05) than piglets in the CON treatment. Blood lymphocytes and nutrient digestibility of sows, as well as the weaning BW and the ADG of piglets were increased linearly (P < 0.05) as the concentration of levan increased in the diet. The diarrhea score of piglets and the fecal gas emissions of sows decreased linearly (P < 0.05) as dietary levan increased. In conclusion, levan supplementation did not affect feed intake or decrease back fat loss of lactating sows, but it improved nutrient digestibility and immune ability, decreased fecal noxious gas emissions of lactating sows, and it improved growth performance and reduced diarrhea 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) | 0.001 | 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".