JC Virus Genotypes in Northwestern China: Implications for Its Population History.
Bibliographic record
Abstract
Genotyping of urinary JC virus (JCV) DNAs is a useful means of elucidating the origin of ethnic populations. We previously reported JCV genotypes in eastern China and Mongolia. To gain a comprehensive picture of JCV genotypes in China, we collected urine samples at three northwestern sites along the Silk Road: X'ian, Lanzhou and Urumqi. DNA was extracted from urine samples, and used to amplify the 610-base-pair region (IG region) of the viral genome. For each geographical site, we determined 16 to 24 IG sequences, from which a neighbor-joining phylogenetic tree was constructed to classify detected JCV isolates into distinct genotypes. (1) The northeastern-Asian genotype (CY) was mainly detected at X'ian and Lanzhou. This finding suggested that these two sites were colonized mainly by northeastern Asians. (2) The northeastern-Asian (CY) and central/western-Asian genotype (B1-b) were mainly detected at Urumqi. This suggested that Urumqi was colonized by both northeastern and central Asians. (3) In addition, several minor JCV genotypes were detected at these sites. These included a genotype (B1-c) prevalent in Europe and western Asia and a genotype (Af2) prevalent in Africa and western Asia. Significant admixture of human populations may have occurred in areas along the Silk Road that was used in ancient times to transport goods between China and Europe.
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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 | 0.001 |
| 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".