Genetic diversity among Canadienne, Brown Swiss, Holstein, and Jersey cattle of Canada based on 15 bovine microsatellite markers
Bibliographic record
Abstract
The genetic diversity among Canadienne, Brown Swiss, Holstein, and Jersey cattle was estimated from relationships determined by genotyping 20 distantly related animals in each breed for 15 microsatellites located on separate chromosomes. The Canadienne, Holstein, and Jersey cattle had an average of six alleles per loci compared with five alleles for Brown Swiss. Furthermore, a number of potentially breed-specific alleles were identified. The allele size variance among breeds was similar, but varied considerably among loci. All of the loci studied were equally heterozygous, as were Brown Swiss, Canadienne, and Holstein cattle (0.68-0.69) whereas Jersey cattle showed lower heterozygosity (0.59). The within-breed estimates of genetic distance were greater than zero and significant. The genetic distance between Canadienne and Holstein (0.156), Brown Swiss (0.243), and Jersey (0.235) was negligible, suggesting close relationship. Concurrently, Brown Swiss and Holstein (0.211) cattle also demonstrated close relationship. In contrast, the Jersey breed was genetically distant from the Brown Swiss and Holstein cattle (0.427 and 0.320, respectively). The characterization of Canadienne cattle, as part of the genetic resource conservation effort currently underway in Canada, underscores the difficulty in scientifically establishing unique breeds. Therefore, the need to consider all relevant morphological characteristics and production performance in combination with available cultural, historical, pedigree, and molecular information becomes relevant when identifying breeds for conservation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".