Maternal and paternal diversity in Xinjiang Kazakh population from China
Bibliographic record
Abstract
The ancient silk road of China passed through Xinjiang and facilitated gene exchanges from the East and the West which impacted on the genetic variation and structure of the nomadic Kazakh population residing there. In order to understand the nature of this genetic variation, 151 Xinjiang Kazakh samples were obtained from four main Kazakh groups and were analyzed using mtDNA and Y-chromosome markers. The Xinjiang Kazakh population is heterogeneous, showing the coexistence of matrilineal lineages with different origins. No genetic differentiation of mtDNA is observed among the four different regional Xinjiang Kazakh populations in Xinjiang by AMOVA and Networks. The genetic diversity of Y-STR loci is higher in Xinjiang Kazakhs (0.968 ± 0.014) than the Kazakhs from Kazakhstan (0.629 ± 0.071) and Russia (0.835 ± 0.020). East Eurasians make a more than 50% contribution to the maternal and paternal lineages of Xinjiang Kazakhs. There is more gene flow from West Eurasian into the maternal lineages of Xinjiang Kazakh than to the Kazakhs from Russia and Kazakhstan. Moreover, mtDNA and Y-STR displayed high polymorphism in Xinjiang Kazakhs (the haplotype diversity and power of discrimination were 0.990 ± 0.003, 0.9137 for mtDNA HVS and 0.968 ± 0.014, 0.9489 for Y-STR system, respectively) suggesting they would be very useful and important markers for forensic analysis and population genetic studies.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".