Evaluation of Egg Quality Traits of Three Indigenous Chicken Ecotypes Kept Under Farmers’ Management Conditions
Bibliographic record
Abstract
Background and Objective: Understanding egg quality traits of indigenous chicken ecotypes under farmers’ management conditions is very important for designing and implementing environment friendly and community based holistic genetic and performance improvement strategies. This study was conducted to compare three ecotypes (lowland, midland and highland) in Tigray, Ethiopia in terms of their external and internal egg quality traits and to determine relationships among these traits. Materials and Methods: From each ecotype, 50 fresh eggs were used to measure external egg quality traits (egg weight, shell weight, shell ratio, egg length, egg width and egg shape index) and internal egg quality traits (albumen weight, albumen ratio, yolk weight, yolk ratio, albumen height, yolk height, yolk to albumen ratio, Haugh unit and yolk color). One-way ANOVA was used to compare the ecotypes and correlation analysis was used to determine relationships among the traits. Results: Three ecotypes were significantly different on all traits except shell ratio and yolk color. In all other external traits, highland ecotype had higher mean values than midland and lowland had the lowest mean values. Highland also had higher mean values in the internal traits other than albumen ratio whereas lowland had higher mean values than both highland and midland ecotypes. The strength of the significant correlations among the external traits, among internal traits and between external and internal traits varied with ecotype. Conclusion: The differences in the quality of local chicken eggs from the three ecotypes suggest that there is a need for customized genetic and performance improvement strategies.
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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".