Effect of Egg Size on Hatchability and Subsequent Growth Performance of Fayoumi Chicken
Bibliographic record
Abstract
The study was conducted to determine effect of egg-weight on hatchability and subsequent growth performance of Fayoumi breeds. A total of 576 eggs were purposively selected and arranged into three groups of small, medium and large sizes each with 192 eggs. Each egg group was randomly sub-divided into three replicates of 64 eggs in a CRD. Eggs were incubated for 21 days and chicks hatched on the same day were counted and individually weighed. Chicks were intensively raised on deep litter system for eight weeks on same diet, but kept separately according to their initial treatment of eggs. Data was analyzed by GLM of SAS and separated for means by Duncan’s multiple-range test. The study result revealed that egg size had effect on hatchability and strongly influenced all parameters measured during the brooding periods. It has significant effect on day-old weight, body weight, final weight gain, final feed conversion and mortality. Chicken producers may opt for medium-sized eggs principally for the purpose of better hatchability and feed conversion ratio whereas large sized eggs for better hatchling weight, weight gain and survivability. It is also recommended that future work may also address the effect of egg size on the same parameters at grower or pullet stage performance and specific diseases which cause paramount chick mortality should be identified.
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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) | 0.000 | 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".