Effect of Age on Production Performance, Egg Geometry and Quality Traits of Lakha Variety of Aseel Chicken in Pakistan
Bibliographic record
Abstract
Aseel chicken is indigenous to Asian subcontinent famous for its vigor, body characteristics and fighting behavior. Moreover, these birds are also able to withstand harsh climatic conditions of tropical and sub-tropical areas but its rearing is getting less importance due to its poor egg production. The present study was conducted to evaluate the effect of age on production performance, egg geometry and egg quality traits of Lakha variety of Aseel maintained under standard managemental conditions at Indigenous Chicken Genetic Resource Center (ICGRC), University of Veterinary and Animal Sciences, Lahore, Pakistan. For this purpose, a total of 42 birds, 14 birds in each of the following age group (30, 70 and 110 week), were kept up to four months. Collected data were analyzed under Completely Randomized Design (CRD) with comparison of means using Fisher’s LSD test. Statistical analyses revealed significantly higher egg production and egg mass in first age group (30 week), higher egg weight in third age group (110 week) and better FCR/ dozen eggs and FCR/ kg egg mass in second (70 week) and third age (110 week) groups with non-significant differences in feed intake. Regarding egg geometry, all parameters were significantly higher in third age group than that of second and first age groups. In egg quality, Haugh unit score was significantly higher in third age group while yolk index and shell thickness showed non-significant differences among all age groups. So it can be concluded that with increasing age, egg production and egg mass decreases with increase in egg geometry traits.
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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".