Short communication: Canine hip dysplasia in Tibetan terriers
Bibliographic record
Abstract
CANINE hip dysplasia (CHD) arises from incorrect coxofemoral joint development in dogs. CHD has been described in many dog breeds, but is generally considered a problem associated with larger breeds, suggesting CHD may have a genetic basis. This has resulted in several investigations into breed-specific studies (Wood and others, 2000, Lewis and others 2010) which have supported the hypothesis that CHD arises due to a combination of polygenic interactions (Wilson and others 2011). These have resulted in a variety of conclusions: differences in heritability scores between breeds, for example, labrador retrievers (Wood and others 2002) and Gordon setters (Wood and others 2000); reports of differences between left and right hips (Tsai and others 2007) with others reporting symmetry of hip scores (Wilson and others 2011) and differences in maternal versus paternal effects (Wood and others 2000). These differences between breeds suggest that there may be minor differences in the major causal genes/alleles in different breeds, meaning that it is essential to study CHD in as wide a range of breeds in order that the condition may be better understood at the species level. As mentioned above, although CHD is often perceived to be a problem affecting larger dogs, it is not restricted to larger breeds and can be found in smaller breeds too. The current work examines the condition in an example of a smaller breed; Tibetan terriers.....
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".