A colony of dog guides: analysis of the genetic variability assessed by pedigree data
Bibliographic record
Abstract
The study presents the analysis of the genetic variability in a colony of dog guides. Three breeds, Labrador (L), Golden Retriever (GR), and German Shepherd (GS), were evaluated. Pedigrees data on 370 L, 260 GR, and 85 GS dogs bred for guideby the National Guide Dog School (SNCG) of Scandicci (Florence, Italy) were used. Data were available beginning from 1994. The average coefficient of coancestry and the mean F were 0.8% and 0.45% in GR, 0.7% and 0.38% in L, 1.0% and 0.49% in GS, respectively. The rate of increase in inbreeding was lower in L population (0.17) than in GR population (0.54), while in GS only the dogs with 5 e 7 traced generations resulted inbred. The results of this research point out that the genetic management of the dogs seems to be carefully and rationally monitored. Nevertheless, the population that may require a greater attention seems to be the GR, where a higher increase of the coefficient of inbreeding per generation is observed; therefore, the importation of germplasm from other working dogs is desirable in order to avoid in future an excessive increase of the inbreeding that could lead to adverse consequences for dogs health and fertility.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".