Retroviral analysis reveals the ancient origin of Kihnu native sheep in Estonia: implications for breed conservation
Bibliographic record
Abstract
Native animal breeds constitute an invaluable pool of genetic resources in a changing environment. Discovering native breeds and safeguarding their genetic diversity through specific conservation programs is therefore of high importance. Endogenous retroviruses have proved to be a reliable genetic marker for studying the demographic history of sheep (Ovis aries). Previous research has revealed two migratory episodes of domesticated sheep from the Middle East to Europe. The first episode included predominantly 'primitive populations', while the second and most recent is hypothesised to have included sheep with markedly improved wool production. To examine whether the recently discovered Kihnu native sheep in Estonia have historically been part of the first migratory episode and to what extent they have preserved primitive genetic characters, we analysed retroviral insertions in 80 modern Kihnu sheep and 83 ancient sheep from the Bronze Age to Modern Period (850 BCE-1950 CE). We identified that the Kihnu sheep have preserved 'primitive', 'Nordic', and other 'ancient' retrotypes that were present both in archaeological and modern samples, confirming their shared ancestry and suggesting that contemporary Kihnu native sheep originate from the first migratory episode. However, over the course of history, there has been a gradual decrease in the frequency of primitive retrotypes. Furthermore, Kihnu sheep possessed several 'novel' retrotypes that were absent in archaeological individuals, but were shared with improvement breeds, suggesting recent crossing within the last two centuries. To preserve these ancient lineages, our results are being applied in the conservation program of the Kihnu Native Sheep Society.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".