Population structure and genetic diversity of worldwide Nova Scotia Duck Tolling Retriever and Lancashire Heeler dog populations
Bibliographic record
Abstract
The aim of this study was to research the population structure and genetic diversity of the Nova Scotia Duck Tolling Retriever (NS) and the Lancashire Heeler (LH) dog breeds. Data consisted of nearly all the worldwide registration history for both breeds, including 28,668 NS and 4,782 LH individuals. A reference population, including the females born between 1999 and 2008, was defined for genetic analyses for each breed. Average depth of the pedigrees known for the reference population dogs was 12.9 complete generation equivalents in the NS and 6.0 in the LH. Only a small fraction of the born dogs were used later for breeding. Effective number of founders was 9.8 in the NS and 15.2 in the LH. More than 50% of the genetic diversity in the reference population was explained by two ancestors in the NS and five in the LH. Average inbreeding coefficients in the reference populations were 0.26 in the NS and 0.10 in the LH. Average kinships were 0.26 and 0.08 and realised effective population sizes 18 and 28, respectively. Failure to use available genetic resources for sustainable breeding has resulted in depletion of genetic variation in both breeds. To increase genetic variation, a larger proportion of the dogs should be used in reproduction and the contributions of reproducing animals should be equalized. In the LH, it is necessary to use the unregistered farm dogs in breeding. In the NS, crosses with another breed are needed.
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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 | 0.000 | 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".