Effect of inbreeding on fertility traits in five dog breeds
Bibliographic record
Abstract
The aim of the study was to analyze retrospectively the influence of inbreeding on fertility traits in five dog breeds: German Shepherd dog (GSD), Golden (GR) and Labrador (LR) Retrievers, Beagle and the Tatra Shepherd dog (TSD). The data were 436 litters, with the total of 2560 puppies: 1307 males and 1206 females. The parents of the litters were 163 dogs and 228 bitches. For each litter the litter size, number of male and female puppies, sex ratio, and sex difference were calculated. The fixed effects of breed, of litter birth year and linear regression coefficients on litter and parents' inbreeding were included in the linear model for litter traits. The correlations between litter traits and litter parents' inbreeding were also estimated. The average litter size was 5.87 (± 2.53) for all breeds. GSD had the smallest average litter size differences in years and the lowest fluctuations of sex ratio with litter size. In other dog breeds those differences were much bigger. The difference between the number of male and female offspring in a litter depended on the breed. The lowest percentage of inbred parents was found for LR, and the highest for TSD. Mating non-inbred animals, in most cases also unrelated, was frequent in all breeds. The inbreeding level of parents had significant influence on the litter traits only for TSD. For the Beagles low, positive and significant correlation between the number of female offspring in a litter and the dam's inbreeding level and the sex ratio below 0.5 suggests sex ratio disturbance. The correlation coefficients between litter inbreeding and litter size for majority of examined dog breeds were positive but not significant. The conclusion is that in Poland at first obligatory monitoring of the inbreeding level for all breeds should be applied.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".