Genetic improvement of hip-extended scores in 3 breeds of guide dogs using estimated breeding values: Notable progress but more improvement is needed
Bibliographic record
Abstract
Two hip quality phenotypes-a hip-extended score assigned by a board certified radiologist and the PennHIP distraction index-were analyzed to estimate genetic parameters and to calculate estimated breeding values used for selecting replacement breeders. Radiographs obtained at 12-18 months of age were available on 5,201 German Shepherd Dogs, 4,987 Labrador Retrievers and 2,308 Golden Retrievers. Obtained by fitting a two-trait model using Bayesian techniques, estimates of heritability for the hip-extended score were 0.76, 0.72, and 0.41 in German Shepherd Dogs, Labrador Retrievers, and Golden Retrievers, respectively, while estimated heritabilities for distraction index were 0.60, 0.66 and 0.59, respectively. Genetic correlations between the two hip quality measures were -0.28 in German Shepherd Dogs, -0.21 in Labrador Retrievers, and -0.29 in Golden Retrievers. Genetic selection for improved hip quality based upon the hip extended score phenotype began in 1980. Among first generation puppies, 34% of 273 German Shepherd Dogs, 55% of 323 Labrador Retrievers, and 43% of 51 Golden Retrievers had an Excellent hip extended score. After 8 generations of selection, mostly based on estimated breeding values derived from the hip extended score, over 93% of 695 German Shepherd Dogs, 94% of 528 Labrador Retrievers, and 87% of 116 Golden Retrievers received an Excellent hip extended score. With respect to PennHIP distraction index values among these same dogs, median values were at or above 0.30 for all 3 breeds meaning that half or more of dogs possessing the Excellent hip-extended-score phenotype remained susceptible to developing the osteoarthritis of canine hip dysplasia. Genetic improvement of the hip-extended-view phenotype to its desired biological endpoint left a surprising proportion of dogs expressing sufficient joint laxity to place them in an osteoarthritis at-risk state as they age. Only by directly applying selection pressure to reduce distraction index was marked reduction in joint laxity noted.
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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 | 0.002 | 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".