The Effect of Hydrolyzed Render Meal, Enzyme Treated Swine Skin Meal, and Cattle Skin Meal on Egg Production Performance, Eggshell Quality, Egg Quality, and Blood Characteristics in Laying Hens
Bibliographic record
Abstract
The trial was conducted to investigate effect of hydrolyzed render meal and skin derived protein meal on the egg production performance, eggshell quality, egg quality and blood characteristics in laying hens. A total of 280 44 week (wk) old (Hy-Line brown) laying hens were used in this 6- wk trial. Birds were randomly assigned to 1 of 4 treatments, 1) BD, basal diet; 2) HRM, basal diet with 2% hydrolyzed render meal; 3) SSM, basal diet with 2% swine skin meal; 4) CHM, basal diet with 2% cattle hide meal with 14 replications per treatment and 5 adjacent cages as replications. During wk- 44 to 46, the egg production (%) was significantly higher (P < 0.05) in SSM treatment (96.37 and 97.10) than others. Overall, egg production was higher (P < 0.05) in SSM treatment (96.79%) than CHM, HRM and BD treatments (96.16%, 95.90% and 95.30%) respectively. In case of average daily feed intake (ADFI), SSM showed significant higher (P < 0.05) value (120g) than BD treatment (117 g). Both SSM and CHM treatments seemed higher (1.009) egg gravity than BD treatment (p < 0.05) at 50- wk. On the other hand, at the same time, egg weight was significantly higher (P < 0.05) in BD treatment compared to others. The egg quality and blood characteristics were unaffected (p > 0.05) by dietary treatments. In a nut shell, the skin derived protein meal supplemented in laying hen diet at 2% enhanced the egg production performance of laying hens and inoffensive to the egg quality and laying hen health status.
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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 | 0.000 |
| Scholarly communication | 0.000 | 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".