Population genetic structure and performing assignment test on six Iranian native goats using simple sequence repeat markers
Bibliographic record
Abstract
The native goat breeds could be better managed and their genetic diversity to be conserved through identification of population genetic structure. Total of 299 animals from six goat breeds, which are major native breeds of Iran, were used to study their genetic structure and understand relationship among the breeds using SSR markers on 13 microsatellite loci. The breeds were selected from different geographic regions of Iran. The results indicated that there is high genetic diversity at the population level (HS of 0.78) and at the species level (HT of 0.86). The level of inbreeding was low across the breeds and even genetic diversion was observed among them, indicating a low level of gene flow at the regional scale. Some level of admixtures was observed among breeds, which supported by clustering of the breeds based on their geographic origin. Analysis of the population genetic structure indicated that all breeds are grouped into four clusters. The assignment accuracy per locus ranged from 40.1% (BM4621) to 66.9% (oarJMP23). The assignment power of microsatellites based on the Bayesian method had positive correlation with the number of alleles and gene differentiation coefficient (Gst) per locus. In conclusion, this study provided a genetic profile for the conservation and improvement and origin of the studied breeds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".