Merging pedigree databases to describe and compare mating practices and gene flow between pedigree dogs in France, Sweden and the <scp>UK</scp>
Bibliographic record
Abstract
Merging pedigree databases across countries may improve the ability of kennel organizations to monitor genetic variability and health-related issues of pedigree dogs. We used data provided by the Société Centrale Canine (France), Svenska Kennelklubben (Sweden) and the Kennel Club (UK) to study the feasibility of merging pedigree databases across countries and describe breeding practices and international gene flow within the following four breeds: Bullmastiff (BMA), English setter (ESE), Bernese mountain dog (BMD) and Labrador retriever (LBR). After merging the databases, genealogical parameters and founder contributions were calculated according to the birth period, breed and registration country of the dogs. Throughout the investigated period, mating between close relatives, measured as the proportion of inbred individuals (considering only two generations of pedigree), decreased or remained stable, with the exception of LBR in France. Gene flow between countries became more frequent, and the origins of populations within countries became more diverse over time. In conclusion, the potential to reduce inbreeding within purebred dog populations through exchanging breeding animals across countries was confirmed by an improved effective population size when merging populations from different countries.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".