An Account of the Taxonomy of North American Wolves From Morphological and Genetic Analyses
Bibliographic record
Abstract
Abstract The available scientific literature was reviewed to assess the taxonomic standing of North American wolves, including subspecies of the gray wolf, Canis lupus. The recent scientific proposal that the eastern wolf, C. l. lycaon, is not a subspecies of gray wolf, but a full species, Canis lycaon, is well-supported by both morphological and genetic data. This species' range extends westward to Minnesota, and it hybridizes with gray wolves where the two species are in contact in eastern Canada and the Upper Peninsula of Michigan, Wisconsin, and Minnesota. Genetic data support a close relationship between eastern wolf and red wolf Canis rufus, but do not support the proposal that they are the same species; it is more likely that they evolved independently from different lineages of a common ancestor with coyotes. The genetic distinctiveness of the Mexican wolf Canis lupus baileyi supports its recognition as a subspecies. The available genetic and morphometric data do not provide clear support for the recognition of the Arctic wolf Canis lupus arctos, but the available genetic data are almost entirely limited to one group of genetic markers (microsatellite DNA) and are not definitive on this question. Recognition of the northern timber wolf Canis lupus occidentalis and the plains wolf Canis lupus nubilus as subspecies is supported by morphological data and extensive studies of microsatellite DNA variation where both subspecies are in contact in Canada. The wolves of coastal areas in southeastern Alaska and British Columbia should be assigned to C. lupus nubilus. There is scientific support for the taxa recognized here, but delineation of exact geographic boundaries presents challenges. Rather than sharp boundaries between taxa, boundaries should generally be thought of as intergrade zones of variable width.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".