Study of the Population Structure in Schnauzer Dogs
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it