A distance-based analysis of habitat selection by American black bears (Ursus americanus) on the Bruce Peninsula, Ontario, Canada
Bibliographic record
Abstract
Because of their wide-ranging habits, conserving large carnivores such as American black bears ( Ursus americanus Pallas, 1780) often depends on understanding habitat needs beyond the boundary of protected areas. We studied habitat selection by black bears in the vicinity of Bruce Peninsula National Park, Ontario — a small, isolated population whose persistence appears dependent on habitat on lands outside the Park. We used an approach based on Euclidean distances to document seasonal habitat selection at two spatial scales and to identify candidate habitat types for protection. Adult females selected dense mixed forests to establish home ranges within the population range, whereas subadults and yearlings selected dense deciduous forests. Within home ranges, adults selected dense mixed forest in spring–summer and dense deciduous forest in late summer – fall. Subadults selected dense deciduous forest, marsh, dense mixed forest, and water during the spring–summer and avoided developed lands and roads. Yearlings selected dense mixed forest, dense deciduous forest, and sparse forests in spring–summer and dense deciduous forest and dense mixed forest in late summer – fall. The selection of dense deciduous and dense mixed forest stands, especially at the broader scale, suggests that strategies to ensure persistence of this isolated population should focus on protecting the integrity of these stands.
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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".