Genetic and morphological differentiation of wolves (<i>Canis lupus</i>) and coyotes (<i>Canis latrans</i>) in northeastern Ontario
Bibliographic record
Abstract
Gray wolves ( Canis lupus L., 1758), eastern wolves ( Canis lycaon Schreber, 1775), and coyotes ( Canis latrans Say, 1823) are presently managed as a single biological population in primary wolf range in Ontario with the intent of minimizing incidental harvest of wolves. This management strategy is based on the assumption that wolves and coyotes cannot be reliably distinguished because of hybridization, and the resulting restrictions on coyote harvest are unpopular with hunters and farmers. We genetically and morphologically characterized a sample of sympatric wolves and coyotes harvested in the Lesser Clay Belt area of northeastern Ontario in 2006–2009 to test the hypothesis that these species cannot be reliably distinguished. We found that wolves and coyotes were genetically and morphologically distinct, with minimal hybridization between them. Our findings suggest that wolves and coyotes in the sampled area can be reliably distinguished, but further sampling is required to determine the full extent of areas in Ontario where wolves and coyotes are reliably distinguishable. We discuss unresolved issues regarding the feasibility of separate management for these species. We also discuss implications of our findings regarding wolf recovery in the northeastern United States.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 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".