Genomic analysis of wolves from Pakistan clarifies boundaries among three divergent wolf lineages
Bibliographic record
Abstract
Among the three main divergent lineages of gray wolf (Canis lupus), the Holarctic lineage is the most widespread and best studied, particularly in North America and Europe. Less is known about Tibetan (also called Himalayan) and Indian wolf lineages in southern Asia, especially in areas surrounding Pakistan where all three lineages are thought to meet. Given the endangered status of the Indian wolf in neighboring India and unclear southwestern boundary of the Tibetan wolf range, we conducted mitochondrial and genome-wide sequencing of wolves from Pakistan and Kyrgyzstan. Sequences of the mitochondrial D-loop region of 81 wolves from Pakistan indicated contact zones between Holarctic and Indian lineages across the northern and western mountains of Pakistan. Reduced-representation genome sequencing of eight wolves indicated an east-to-west cline of Indian to Holarctic ancestry, consistent with a contact zone between these two lineages in Pakistan. The western boundary of the Tibetan lineage corresponded to the Ladakh region of India's Himalayas with a narrow zone of admixture spanning this boundary from the Karakoram Mountains of northern Pakistan into Ladakh, India. Our results highlight the conservation significance of Pakistan's wolf populations, especially the remaining populations in Sindh and Southern Punjab that represent the highly endangered Indian lineage.
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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 | 0.000 | 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) | 0.001 | 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".