Genetic base and inbreeding of Canadienne, Brown Swiss, Holstein and Jersey cattle in Canada
Bibliographic record
Abstract
Summary The narrowing of the genetic base is of major concern to many cattle breeders and this is a consequence of a small effective population size and an increase in the inbreeding coefficient in cattle populations. Studies of pedigrees of cattle populations found in Canada showed that the effective population size and rate of inbreeding over a 12-year period (1983–94) were 123 and 0.412 percent for Canadienne, 975 and 0.054 percent for Brown Swiss, and 2 183 and 0.024 percent for Jersey cattle populations. Over the same period, the year to year trends in inbreeding coefficients based on co-ancestry was 0.11 percent for Canadienne and 0.07 percent for Brown Swiss cattle populations, and 0.37 percent for Holstein bulls used by the artificial insemination industry. Inbreeding was not found to be a major problem in any of the populations examined. However, only 10 bulls of their respective breed sired 61 percent of the Canadienne, 21 percent of the Brown Swiss, and 29 percent of the Jersey cattle populations, and 41 percent of the Holstein bulls in the bull studs. More and more breeders are demanding proven sires to increase milk production from fewer cows resulting in the narrowing of the genetic base of the national cattle populations. Newer technology that provides precise genetic modification could further contribute to the narrowing of the genetic base compromising the ability to sustain current production and respond to changing markets in the long-term.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".