Forest composition around wolf (<i>Canis lupus</i>) dens in eastern Algonquin Provincial Park, Ontario
Bibliographic record
Abstract
Den-site selection is a poorly understood aspect of wolf (Canis lupus) ecology, particularly for populations in forested ecosystems. Using a geographic information system and remote-sensing imagery, we examined patterns of habitat use around wolf dens in Algonquin Provincial Park, Ontario. Sixteen den sites were sampled for eight habitat types in their immediately vicinity, as well as at radii of 500, 1000, 1500, and 2000 m. We used a resource-selection ratio to determine whether specific habitat types were preferred or avoided at different radii relative to the total proportion of habitat types found within the study area. Wolves established dens in areas with significantly high proportions of pine forest up to and including a 1000-m radius and low proportions of tolerant and intolerant hardwoods within 500 m. We conclude that wolves establish den sites based primarily on the presence of pine forest, a habitat that is frequently logged within Park boundaries and subject to problems with regeneration after cutting. Dens sites are likely not limiting in this population, but our results suggest the need to protect current den sites at a relatively large spatial scale. These results also provide unique information to assess the potential for recolonization and reintroduction of wolf populations in other areas.
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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 | 0.001 |
| 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 | 0.000 |
| 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".