Analyses of genetic diversity in five <scp>C</scp>anadian dairy breeds using pedigree data
Bibliographic record
Abstract
The issue of loss of animal genetic diversity, worldwide in general and in Canada in particular, has become noteworthy. The objective of this study was to analyze the trend in within-breed genetic diversity and identify the major causes of loss of genetic diversity in five Canadian dairy breeds. Pedigrees were analyzed using the software EVA (evolutionary algorithm) and CFC (contribution, inbreeding, coancestry), and a FORTRAN package for pedigree analysis suited for large populations (PEDIG). The average rate of inbreeding in the last generation analyzed (2003 to 2007) was 0.93, 1.07, 1.26, 1.09 and 0.80% for Ayrshire, Brown Swiss, Canadienne, Guernsey and Milking Shorthorn, respectively, and the corresponding estimated effective population sizes were 54, 47, 40, 46 and 66, respectively. Based on coancestry coefficients, the estimated effective population sizes in the last generation were 62, 76, 43, 61 and 76, respectively. The estimated percentage of genetic diversity lost within each breed over the last four decades was 6, 7, 11, 8 and 5%, respectively. The relative proportion of genetic diversity lost due to random genetic drift in the five breeds ranged between 59.3% and 89.7%. The results indicate that each breed has lost genetic diversity over time and that the loss is gaining momentum due to increasing rates of inbreeding and reduced effective population sizes. Therefore, strategies to decrease rate of inbreeding and increase the effective population size are advised.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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".