HEALTH PROBLEMS IN SELECTED BREEDS OF LARGE DOGS
Bibliographic record
Abstract
The aim of the study was to analyse selected large dog breeds in terms of the incidence of their typical diseases. The research part of the study was based on an analysis of questionnaires carried out among owners and breeders of pedigree dogs and veterinarians. The questionnaires asked questions concerning, among other things, the choice of a particular breed for breeding and diseases occurring in dogs. Four large dog breeds were selected for analysis and these were the German Shepherd, Golden Retriever, Labrador Retriever and Bernese Mountain Dog. As a result of the analysis, it can be seen that dog owners are not interested in the health status of a breed before buying a pet, and the main criterion for choosing a dog was disposition and physical appearance. Both dog owners and veterinary surgeons indicated that large breed dogs are most commonly affected by musculoskeletal disorders. The most vulnerable breeds to musculoskeletal injuries or diseases according to owners were the Golden Retriever and the Bernese Mountain Dog. Accord- ing to veterinarians, all large and giant dogs are at risk of musculoskeletal health problems, but the breeds with the highest risk of these diseases are German Shepherds and Bernese Mountain Dogs. Surveys carried out have shown some inappropriate phenomena – the reluctance of some breeders to talk about the health problems of the dogs they keep. Therefore, it seems advisable to promote knowledge of diseases in pedigree dogs, which will allow more thoughtful breeding, if only through appropriate mating, and consequently increase the comfort of the dogs’ lives.
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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 | 0.002 | 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) | 0.002 | 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".