Genetic diversity among Canadienne, Brown Swiss, Holstein and Jersey cattle based on mitochondrial D-loop sequence variation
Bibliographic record
Abstract
Polymorphisms creating 36 unique haplotypes were observed with in breeds at 55 sites in the displacement loop (D-loop) region of the mitochondrial DNA (mtDNA) consisting of 814 bp. The majority (56%) of the differences observed were the result of nucleotide substitution events with 19 transitions, 12 transversions, 11 deletions, 12 insertions and 1 inversion. In all cases, the insertions and deletions were of a single nucleotide. Canadienne cattle were found to have 60% unique haplotypes within the population compared to 89% in Brown Swiss, 90% in Holstein and 100% in Jersey cattle, possibly reflecting the narrow genetic base in the Canadienne breed. The degree of sequence divergence in the D-loop region of mtDNA was based on samples from 20 Canadienne, 9 Brown Swiss, 10 Holstein and 10 Jersey cattle and a phylogenetic analysis showed that these cattle (Bos taurus) were not evolutionarily distinct. All four breeds grouped together when a strict consensus tree was generated. Intra-breed variability proved to be high for the Canadienne, Holstein and Jersey breeds (57–73%) but not the Brown Swiss breed (29%). The Canadienne and Brown Swiss (45%), and Brown Swiss and Holstein (43%) showed the lowest degree of inter-breed variability. The greatest variability among the four breeds was between Canadienne and Jersey (80%) cattle. These findings question the validity of phenotypic assessment of genetic diversity, such as Canadienne cattle being described as “Black Jersey”. Key words: Genetic distance, phylogenetic analysis, D-loop sequence, cattle
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".