Genetic Characterization of Hybrid Wolves across Ontario
Bibliographic record
Abstract
Four ''races'' of wolves have been described in Ontario as follows: 1) Canis lupus hudsonicus inhabiting the subarctic tundra, 2) A race (Ontario type) of the eastern timber wolf (Canis lupus lycaon) that inhabits the boreal forests, 3) A second race (Algonquin type) of C. l. lycaon that inhabit the deciduous forests of the upper Great Lakes, and 4) A small wolf (Tweed type) in central Ontario that has been proposed to be a hybrid between the Algonquin type wolf and expanding coyotes, Canis latrans. Using mitochondrial DNA (mtDNA) control region sequences and 8 microsatellite loci, we developed DNA profiles for 269 wolves from across Ontario. The distribution of mtDNA was predominantly coyote and the eastern wolf, Canis lycaon, in Algonquin Park and the southern Frontenac Axis with a combination of these mtDNA and gray wolf mtDNA in northern Ontario. Bayesian clustering grouped northern Ontario wolves independent of mtDNA with a second grouping of eastern and Tweed wolves from Algonquin. Individual clustering identified 3 groups represented by 1) northern Ontario wolves, 2) eastern wolves, and 3) Tweed wolves from the Frontenac Axis. Genomic representation analyses indicate that the Tweed wolves are hybrids between the coyote and the eastern wolf and represent the Ontario distribution of the eastern coyote, whereas the wolves in the upper Great Lakes region represent products of historic and/or continuing hybridization between C. lycaon and C. lupus. There was low structuring among wolves in these regions, and Algonquin suggesting a larger northern connected metapopulation with gene flow between the Ontario and Algonquin types.
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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 | 0.001 | 0.001 |
| 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".