Environmental Effects and Genetic Parameters for Growth Traits of Lohi Sheep
Bibliographic record
Abstract
Estimating genetic parameters for growth traits is crucial to plan breeding strategies for improving meat production in indigenous sheep breeds. The study first tested the effects of environmental and maternal effects on five growth traits, including birth weight (BWT), weight at 120 days (WT120), weight at 180 days (WT180), weight at 270 days (WT270) and weight at 365 days of age (WT365) and then estimated genetic parameters for these traits using data obtained in 1215 Lohi sheep. The effects of factors, including year (YOB), season (SOB) and type of birth (TOB), age of dam (AOD) and sex on growth traits of Lohi sheep, were examined using analysis of variance (ANOVA) in R software. Sex, TOB and YOB significantly affected all studied traits. The estimates of direct and maternal heritability for BWT and WT120 were 0.15 ± 0.08 and 0.20 ± 0.06, and 0.45 ± 0.16, 0.21 ± 0.08, respectively. The direct heritability estimates for WT180, WT270 and WT365 were 0.20 ± 0.07, 0.21 ± 0.07 and 0.19 ± 0.08, respectively. Due to the high heritability estimate obtained for WT120 compared to other studied traits, and its strong genetic correlation (>0.9) with post-weaning growth traits, it is recommended that selection must be practiced on WT120 to improve the growth performance of Lohi sheep. The results could be used for the development of genetic/genomic selection programs aiming to improve the production performance of the Lohi sheep.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".