Genetic management of Dutch golden retriever dogs with a simulation tool
Bibliographic record
Abstract
Excessive inbreeding rates and small effective population sizes are an important problem in many populations of dogs. Proper genetic management of these populations can decrease the problem, and several measures are available. However, the effectiveness of these measures is not clear beforehand. Therefore, a simulation model was developed to test measures that aim to decrease the rate of inbreeding. The simulation program was used to evaluate inbreeding restriction measures in the Dutch golden retriever dog population. This population consisted of approximately 600 dams and 150 sires that produce 300 litters each year. The five most popular sires sire approximately 25% of the litters in a year. Simulations show that the small number of popular sires and their high contribution to the next generation are the main determinants of the inbreeding rates. Restricting breeding to animals with a low average relatedness to all other animals in the population was the most effective measure and decreased the rate of inbreeding per generation from 0.41 to 0.12%. Minimizing co-ancestry of parents was not effective in the long run, but decreased variation in inbreeding rates. Restricting the number of litters per sire generally decreased the generation interval because sires were replaced more quickly, once they met their restriction. In some instances, this lead to an increase in inbreeding rates because the next generations were more related. The simulation tool proved to be a powerful and educational tool for deciding which breeding restrictions to apply, and can be effective in different breeds and species as well.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".