Effect of whole wheat, enzyme supplementation and grain texture on the production performance of laying hens
Bibliographic record
Abstract
To determine the effects of a modified laying hen diet/whole wheat combination, grain texture and enzyme (arabinoxylanase preparation) supplementation on production performance, 384 White Leghorn hens were fed either a nutritionally balanced diet or a modified diet/whole wheat (80%/20%) combination from 20 to 64 wk of age. The modified diets were formulated to ensure the calcium and available phosphorus requirements were met. Primary grains corn and wheat were ground through either 5- or 7-mm screens. Except for weeks 44–48, there was no effect (P > 0.05) of feeding the modified diet/whole wheat combination on egg production to 56 wk. Egg production was reduced during the 44–48 and 56–60 wk periods when the modified diet/whole wheat combination (90.1% and 84.3%; 44–48 and 56–60 wk) was supplemented with the commercial enzyme (84.8% and 76.2%; 44–48 and 56–60 wk). For hens fed the modified diet/whole wheat combination, 7 mm ground grains in the modified diet reduced (P ≤ 0.05) egg production (83.2% and 76.5%; 5 mm and 7 mm grains) during the 60–64 wk period. Egg specific gravity was not affected (P > 0.05) by treatment. Layer diets mixed with whole wheat or containing coarsely ground grains could result in decreased feed costs and increased returns for producers. Key words: Whole wheat, enzyme, grain texture, layer
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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 | 0.000 |
| 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".