The use of exogenous enzyme supplementation in hulless barley based diets for laying hens
Bibliographic record
Abstract
In experiment 1, five diets formulated from wheat and four hulless barley (Silky, Falcon, Gainer and Dawn) cultivars were fed to two strains of hens (Hyline W36 and Dekalb Sigma) to measure production performance. Results showed that the hulless barley cultivars Silky, Gainer and Dawn can effectively replace wheat without any loss of egg mass, while all four hulless barley cultivars can replace wheat without any effect on feed efficiency or feed intake. In experiment 2, each of the diets in trial one, were formulated either with or without an exogenous enzyme cocktail and fed to Shaver White laying hens. Results showed that enzyme supplementation resulted in significant improvements (P < 0.05) in egg mass and feed efficiency, and small insignificant increases in egg production and egg weight over hens fed the unsupplemented diets. For experiment 3, the dietary levels of some major nutrients (P, lysine and methionine) were lowered by up to 10% in the presence of appropriate exogenous enzymes and fed to laying hens. The results showed that, compared to a regular diet (without exogenous enzymes) feeding reduced levels of P, lysine and methionine in the presence of exogenous enzymes, did not result in any significant reduction (P > 0.05) in egg production, egg mass and feed efficiency. (Abstract shortened by UMI.)
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".