Analysis of founder and ancestor contribution to the golden and Labrador retriever populations in the area of Cracow Branch of the Polish Kennel Club.
Bibliographic record
Abstract
The study examines the founder and ancestor contribution to the active population of Golden retrievers (GR) and Labrador retrievers (LR) recorded in the herdbook of Cracow Branch of the Polish Kennel Club. Pedigrees of 192 GR dogs (84 males and 108 females) born in 1998-2007 and 272 LR (110 males and 162 females) born in 1997-2007, were used. The effective numbers of founders were 52 and 96 for GR and LR, respectively. In the GR reference population the contribution of 22 main founders explained about 51% of the gene pool. Four founders contributed from 2% to ca. 6% of genes; the others only 1-2%. Of the 23 main founders in the LR population four top founders contributed from 2% to 4% of genes, while the rest only 1-2%. The group of the LR main founders explained about 41% of the gene variation. In the GR population 28 main ancestors explained over 71% of their gene pool. Four GR ancestors made from 3% to over 9% gene contribution. The rest of the GR ancestors contributed 1-3%. Also 28 main ancestors were found in the LR reference population, with contributions explaining over 63% of variation. Six of them had the highest gene contribution – from 3% to 6.5%, the next 5 – from 2% to about 3%, and the rest 1-2%. Six animals in the GR reference population, and 5 in the LR, were both main founders and main ancestors. At present, the gene pools of both populations are not endangered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".