Influence of a cocktail of protease and xylanase in different energy densities of corn- and soybean-meal-based diet on growth performance, nutrient digestibility, carcass quality, and gas emission in broilers
Bibliographic record
Abstract
A total of 480 one-day-old male Ross 308 broilers with an average initial body weight (BW) of 42 ± 1 g was randomly divided into four treatments (8 replicates/treatment, 15 birds/replicate) by their BW to evaluate the influence of a cocktail of protease and xylanase in different energy density of corn- and soybean-meal-based diet on broilers. This experiment lasted for 35 d and there were two phases; starter (1–18 d) and finisher (19–35 d). The experiment was a 2 × 2 factorial arrangement with two levels of metabolizable energy and the cocktail enzyme with 200 mg of protease kg−1 diet and 100 FXU xylanase kg−1 diet. Both high-nutrient density and cocktail enzyme supplementation increased (P < 0.05) the body weight gain (BWG) during day 1–18 and day 1–35 as well as dry matter retention on day 35. Broilers fed the high-nutrient-density diets had higher (P < 0.05) energy retention on day 35 and relative breast muscle weight. Broilers fed the cocktail enzyme diets significantly increased nitrogen (N) retention and decreased the levels of ammonia (NH3) and hydrogen sulphide (H2S) emission. In conclusion, dietary supplementation of the cocktail enzyme component improved BWG, N retention, and reduced excreta noxious gas emission in 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) | 0.001 | 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) | 0.000 | 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".