Effects of Feeding Locally Grown Whole Barley With or Without Enzyme Addition and Whole Wheat on Broiler Performance and Carcass Traits
Bibliographic record
Abstract
Two experiments were conducted to investigate the impact of increasing dietary levels of whole barley (WB) with or without exogenous enzymes and of whole wheat (WW) without E fed from 7 d of age, on performance and carcass characteristics of broilers. Experiment 1 was conducted with corn-soybean meal grower diets containing WB at 0, 10, 10 + enzymes, 15, or 15% + enzymes. The finisher diets contained, as fed, WB at 0, 15, 20 + enzymes, 15, or 20% + enzymes. In Experiment 2, grower diets contained 0, 10, 10, 20, or 20% WW with 0, 20, 35, 20, or 35% WW in the finisher diets. No enzymes were used for WW diets. In each Experiment, 1,500 1-d-old Ross x Ross male broilers were randomly distributed in 30 floor pens of 50 birds each. Six replicates were allotted to each treatment. Body weight, average daily gain (ADG), feed intake (FI), and feed efficiency ratio (FER) were measured at 7, 21, and at 38 d of age. In Experiment 1, ADG was lower (P < 0.05) in the control vs. Diet 5. However, FER with enzyme addition was lower, and FI with enzymes was higher (P < 0.05). Final BW, gizzard, and pancreas weights were higher (P < 0.05) with WB inclusion. In Experiment 2, ADG and BW significantly increased with addition of WW, although the response was best for Diets 2 and 3. Abdominal fat and carcass weights increased (P < 0.05) with the WW levels in the diets.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".