Genomic evaluation, breed identification, and population structure of Guernsey cattle in North America, Great Britain, and the Isle of Guernsey
Bibliographic record
Abstract
As of December 2015, 2,376 Guernsey bulls and cows had genotypes from collaboration between the United States, Canada, the United Kingdom, and the Isle of Guernsey. Of those, 439 bulls and 504 cows had traditional US evaluations, which provided sufficient data to justify investigation of the possible benefits of genomic evaluation for the Guernsey breed. Evaluation accuracy was assessed using a traditional 4-yr cutoff study. Twenty-two traits were analyzed (5 yield traits, 3 functional traits, and 14 conformation traits). Mean reliability gain over that for parent average was 16.8 percentage points across traits, which compares with 8.2, 18.5, 20.0, and 32.6 percentage points reported for Ayrshires, Brown Swiss, Jerseys, and Holsteins, respectively. Highest Guernsey reliability gains were for rump width (44.5 percentage points) and dairy form (40.5 percentage points); lowest gains were for teat length (1.9 percentage points) and rear legs (side view) (2.3 percentage points). Slight reliability losses (1.5 to 4.5 percentage points) were found for udder cleft, final score, and udder depth as well as a larger loss (13.6 percentage points) for fore udder attachment. Twenty-one single nucleotide polymorphisms were identified for Guernsey breed determination and can be used in routine genotype quality control to confirm breed and identify crossbreds. No haplotypes that affect fertility were identified from the current data set. Principal component analysis showed some divergence of US and Isle of Guernsey subpopulations. However, the overlap of US, Canadian, UK, and Isle of Guernsey subpopulations indicated the presence of gene flow, and the similarities in the subpopulations supports a common genomic evaluation system across the regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".