Morphological relationships among populations support a single taxonomic unit for the North American Gray Wolf
Bibliographic record
Abstract
Abstract The Gray Wolf (Canis lupus) is viewed as one of the most diverse mammal species. In North America, the diversity of its forms is debated, with views conflicting on subspecies designation. The present study aimed to reinvestigate the skull morphometric variation among North American populations while attempting to unveil underlying causal factors. A large sample of vouchered museum skulls, collected from 12 ecogeographical populations spanning the North American range of the species, was examined and 21 craniodental characters were measured. Skull shape showed within-population variations but provided evidence for a high morphological affinity among populations. Allometric analyses also pointed to similar evolutionary paths among populations. However, significant size-related differentiation was revealed within and among populations. Skull size could be related to three insulin-like growth factor-1 gene (IGF-1) alleles. Ecological conditions that should determine prey type and availability accounted for most of the skull size variation. In contrast, no evidence of geographical isolation of populations was detected. The results support the existence of a single morphological pool of North American gray wolf populations that could be equated with one taxonomic unit. This study raises again the question of the diversity of forms in this species in North America and calls into question the validity of previously recognized species and subspecies based on genetics and morphology.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".