Assessing genetic diversity of various Canadian sheep breeds through pedigree analyses
Bibliographic record
Abstract
The loss of genetic variability in a population will drastically affect the success of a breeding program by reducing selection response and fitness and, consequently, affecting reproduction, resilience, and production efficiency. The objective of this study was to perform an in-depth analysis of the pedigree of the Canadian sheep breeds to assess the levels of inbreeding, effective population size, and other metrics of genetic diversity, which included the five most important sheep breeds in Canada: Dorset, Polypay (PO), Rideau-Arcott, Romanov (RV), and Suffolk, using a large dataset (1 336 926 animals). As measures of genetic diversity, effective population size, inbreeding coefficient, effective number of founders, effective number of founder genomes, effective number of nonfounders, and effective number of ancestors were estimated. The completeness and depth of the Canadian sheep pedigree datasets were reasonably high, with <20% parental information missing. More attention should be given to PO breed, which was found to have the smallest effective population size (55), and RV breed, which had the highest average level of inbreeding (4.8%). Techniques such as optimum contribution selection and minimum coancestry mating could be used to minimize the inbreeding of future generations, while maintaining genetic progress at a desirable level.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".