Effects of lactulose on growth, carcass characteristics, faecal microbiota, and blood constituents in broilers
Bibliographic record
Abstract
This study was conducted to determine the effect of supplementing diets with lactulose on growth performance, carcass characteristics, faecal microbiota, and blood constituents. A total of 324 one-day-old Ross 308 mixed-sex broiler chicks with an average initial body weight of 38 g were used in a 35-day growth assay. There were 18 birds/pen and six pens/treatment with food and water available ad libitum. Treatments consisted of a corn-soybean-meal-based diet with 0, 0.25 and 0.5% of lactulose. The results indicated that body weight gain (BWG) was improved (linear effect, P < 0.05) by increasing the concentration of lactulose in the diet from zero to 0.5% while the feed conversion ratio (FCR) decreased (linear effect, P < 0.05) for Days 8 to 21, 21 to 35, and overall (Day 0 to 35). Chickens fed the diet supplemented with 0.5% lactulose showed a higher relative weight of breast meat compared to other groups. Inclusion of lactulose decreased the count of Salmonella and E. coli in excreta of chickens fed diets containing 0.25 or 0.5% lactulose (P < 0.05), but the count of Lactobacillus was not affected. Drip loss percentage was decreased (P < 0.05) on Day 1 by addition of 0.5% lactulose, but there was no effect on meat colour. Blood characteristics were not influenced. Thus, it was concluded that inclusion of lactulose improves growth performance and alters excreta microbial populations with no adverse effect on broilers.
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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".