Effects of levan‐type fructan on growth performance, nutrient digestibility, diarrhoea scores, faecal shedding of total lactic acid bacteria and coliform bacteria, and faecal gas emission in weaning pigs
Bibliographic record
Abstract
Abstract BACKGROUND The use of antibiotics as growth promoters in feed has been fully or partially banned in several countries. The objective of this study was to evaluate effects of levan‐type fructan on growth performance, nutrient digestibility, faecal shedding of lactic acid bacteria and coliform bacteria, diarrhoea scores, and faecal gas emission in weaning pigs. A total of 144 weaning pigs [(Yorkshire × Landrace) × Duroc] were randomly allocated to four diets: corn–soybean meal‐based diets supplemented with 0, 0.1, 0.5, or 1.0 g kg−1 levan‐type fructan during this 42‐day experiment. RESULTS During days 0 to 21 and 0 to 42, average daily gain and average daily feed intake were linearly increased (P < 0.01) with increasing dietary levan‐type fructan inclusion. The apparent total tract digestibility of dry matter, crude protein, and gross energy were linearly increased (P < 0.001) with increasing dietary levan‐type fructan content. With increasing levels of levan‐type fructan, faecal lactic acid bacteria counts were linearly increased (P = 0.001). CONCLUSION The results indicate that dietary supplementation with increasing levan‐type fructan enhanced growth performance, improved nutrient digestibility, and increased faecal lactic acid bacteria counts in weaning pigs linearly. © 2017 Society of Chemical Industry
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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 | 0.001 |
| 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".