Mitochondrial DNA extracted from eastern North American wolves killed in the 1800s is not of gray wolf origin
Bibliographic record
Abstract
We analysed the mitochondrial DNA (mtDNA) from two historical samples of eastern North American wolves: the last wolf reported to have been killed in northern New York State (ca. 1890s) and a wolf killed in Maine in the 1880s. These wolves represent eastern wolves, presently classified as the gray wolf (Canis lupus) subspecies Canis lupus lycaon, which were present well before the expansion of western coyotes (Canis latrans) into these regions. We show the absence of gray wolf mtDNA in these wolves. They both contain New World mtDNA, supporting previous findings of a North American evolution of the eastern timber wolf (originally classified as Canis lycaon) and red wolf (Canis rufus) independently of the gray wolf, which originated in Eurasia. The presence of a second wolf species in North America has important implications for the conservation and management of wolves. In the upper Great Lakes region, wolves of both species may exist in sympatry or interbreed with each other, which impacts the accuracy of estimates of numbers of wolves of each species within this geographic region. Furthermore, the historical distribution of the eastern timber wolf (C. lycaon), as revealed by these skin samples, has important implications for the reintroduction of wolves into the northeastern U.S. states, such as New York and Maine.
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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 | 0.001 | 0.001 |
| 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".