Genetic analysis of ten sheep breeds using microsatellite markers
Bibliographic record
Abstract
The genetic variability of 257 sheep from 10 breeds; North Country Cheviot (NCC), Cheviot (CHE), Dorset (DOR), Suffolk (SUF), Scottish Blackface (SBF), Texel (TEX), Romanov (ROM), Finnish Landrace (FIN), Icelandic (ICE) and Red Masai (MAS) was assessed using 10 microsatellite loci. The average number of alleles per locus was 4.3 in ROM, 5.0 in MAS, and spanned a narrow range (5.4 to 6.0) in the other breeds. Estimates of expected heterozygosity (H E ) of the breeds varied within 0.05 point of each other (0.62 in FIN to 0.67 in CHE), except for ROM (0.53) which was lower (P < 0.05) than those of the other breeds, and in MAS (0.57), which was lower (P < 0.05) than those of NCC, CHE and SBF. Estimates of observed heterozygosity (H O ) of the breeds were the lowest in MAS, ROM and SUF (0.50 to 0.53) and the highest in NCC and CHE (0.64 and 0.67). The H E was greater (P < 0.01) than H O only in SUF. The results suggest that there have not been drastic losses of genetic variability in the intensely selected breeds. The low genetic variability of ROM was probably due to a small number of animals imported to North America. The British breeds (NCC, CHE, SUF, DOR, SBF) were genetically close to each other, as were the North European breeds (ROM, ICE, FIN). MAS was remotely related to the British breeds, but it was surprisingly close to the North European breeds. TEX was more closely related to the British breeds than to the North European breeds. More than 90% of 1000 simulated individuals from each breed were assigned to the correct breed, indicating that this panel of markers is useful for the identification of breed membership of individual animals, and could be used to protect the integrity of registered breeds. Key words: Sheep, genetic variability, genetic distance, microsatellites
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".