Study of the Population Structure in Schnauzer Dogs
Bibliographic record
Abstract
The aim of this study was to evaluate the population structure of a Schnauzer dogs kennel. Pedigree data of 129 dogs were collected from a kennel in Southern Brazil. Dogs were divided into groups by height (“miniature”, “standard”, and “giant”) and subsequently, into coat color subgroups (“not informed”, “salt and pepper”, “black”, “white”, and “black and silver”). Population parameters were estimated using the Contribution, Inbreeding, Coancestry (CFC), and RelaX2 programs. Three ancestral generations were traced from the kennel dogs, totaling 685 unique individuals. Of these, 42% were considered founders. The analysis of the effective number of founders, number of effective ancestors, and inbreeding coefficient means were77, 44.9, and 0.08 for the miniature group, 26, 11.7 and 0.05for the standard group, and 28, 9.9 and 0.12 for the giant group, respectively. The subgroup “salt and pepper” in the “giant” group showed the highest inbreeding coefficient (0.14) and the highest kinship coefficient (0.20). Monitoring inbreeding allows to control upcoming breeding to acquire desirable characteristics in the population minimizing risk of deleterious effects.
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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".