Investigation of genetic variability and inbreeding characteristics in a population of Zandi sheep
Bibliographic record
Abstract
The aim of the present study was to evaluate the genetic parameters and genetics trends for birth and weaning weights, and to quantify the inbreeding characteristics for a population of Zandi sheep. Genetic parameters from both single- and two-trait analyses were estimated using restricted maximum likelihood (REML) with animal models. Genetic trends were estimated by averaging estimated breeding values on year of birth. Full pedigree was analyzed for estimation of inbreeding characteristics. The results obtained confirmed relatively low additive genetic variation in the population, especially for weaning weight. Estimates of direct and maternal heritability were 0.12 and 0.20 for birth weight and 0.08 and 0.02 for weaning weight, respectively. Estimates of breeding value averaged by year of birth for birth weight and weaning weight increased over time. However, there was little genetic progress for birth weight (0.002 kg yr-1), whereas the tendency for weaning weight was 0.020 kg yr-1. Inbred animals consisted of 25% of the population. The effective population size (Ne) was 66 individuals. The inbreeding rate per generation (ΔF) was 0.76%. The average value of inbreeding (F) in the Zandi population was 1.05% and the average relatedness (AR) coefficient reached 1.64% in the whole pedigree. A range of 2.84 to 4.01 yr was obtained for the average generation interval in different pathways. Selection of rams with the lowest AR was recommended for better management of inbreeding and avoidance of relative mating. Key words: Sheep, genetic trends, heritability, inbreeding, average relatedness
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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 | 0.001 | 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".