Population structure, inbreeding trend and their association with hip and elbow dysplasia in dogs
Bibliographic record
Abstract
Abstract The aims of this study were to examine population structure and inbreeding trend in six dog breeds in Finland and to assess the inbreeding depression for hip and elbow dysplasia. Data consisted of 289 569 dogs, of which 36 924 dogs also had a record for hip and/or elbow dysplasia screening. From the early 1980s onwards, inbreeding trends were decreasing in the Golden Retriever, the Labrador Retriever, the Rough Collie and the Rottweiler, probably as a result of importations of dogs, and somewhat increasing in the Finnish Hound and the German Shepherd. When analysed per generation, observed mean inbreeding coefficients were higher than the expected ones in each breed, indicating that breeders have not actively avoided inbreeding. As a class effect, the inbreeding level was significant only for hip dysplasia in the Labrador Retriever and the German Shepherd breeds. As a regression, inbreeding level of a dog had only a minor effect on both of the dysplasias. Hip dysplasia in the Labrador Retriever appeared to be more influenced by longer term aggregation of homozygosity (long-term inbreeding) in animals than by shorter-term inbreeding. When analysed from two data sets with a minimum of five and two ancestral generations for each dog in the data, a statistically significant association between hip dysplasia and inbreeding for the Labrador Retriever could be detected only in the former data set.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".