Effects of Emulsifier and Multi-enzyme in Different Energy Densitydiet on Growth Performance, Blood Profiles, and Relative Organ Weight in Broiler Chickens
Bibliographic record
Abstract
This study was conducted to determine the effects of emulsifierand multi-enzyme in different energy density diet on growth performance, blood profiles, and relative organ weight in broiler chickens. A total of five hundred and forty2-d-oldmale Ross broilers (BW of 42.4 ±1.3 g) wereused in a 35-d experiment and randomly divided into 5 treatment groups: 1) NC [low energy diet, 3% tallow, ME = 3000 (1 to 21 d) and 3100 kcal/kg (22 to 35 d)], 2) PC [high energy diet, 5.5% soybean oil, ME = 3150 (1 to 21 d) and 3250 kcal/kg (22 to 35 d))], 3) P1 (NC+0.1% multi-enzyme), 4) P2 (NC + 0.05% emulsifier), 5) P3 (NC + 0.1% multi-enzyme + 0.05% emulsifier). Multi-enzymecontained ?-galactosidase, galactomannase, xylanase, and beta-glucanase. Emulsifier was a commercial product named Prosol® which wassodium stearoyl-2-lactylate.There were 9 replications per treatment with 12 birds per pen. From d 0 to 21, body weight gain (BWG) in PC and P1 treatments increased (P < 0.05) compared with NC treatment. From d 22 to 35, feed intake (FI) was greater (P < 0.05) in P3 treatment than PC treatment. On d 35, triglyceride concentration in PC, P1 and P3 treatments was greater (P < 0.05) compared with NC treatment. No differences were observed on white blood cell (WBC), red blood cell (RBC) and glucose concentration. The relative weight of the spleen was significantly decreased (P < 0.05) in P3 treatment compared with PC treatment. Furthermore, the relative weight of the bursa of Fabricius in P3 treatment was higher (P < 0.05) than PC, P1 and P2 treatments. In conclusion, the results of this study indicate that emulsifier andmulti-enzymein low-density diets can partially improve growth performance, triglyceride, and relative organ weight in broiler chickens, which can counterpart the negative effects caused by the decreased nutrient concentration.
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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) | 0.000 | 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".