Habitat selection by black bears in an intensively logged boreal forest
Bibliographic record
Abstract
Extensive logging of the boreal forest rejuvenates landscapes once dominated by old-growth stands. As black bear ( Ursus americanus Pallas, 1780) fitness and behavior are known to be primarily related to the abundance of shade-intolerant soft mast species in northern forests, we hypothesized that logging will influence habitat and space use patterns of black bears. We used VHF telemetry on 12 female black bears in the Réserve faunique des Laurentides (Quebec, Canada) to investigate seasonal patterns of habitat selection in an exploited heterogeneous boreal landscape at different spatial scales. Habitat characterization based on seven forest cover types allowed us to compare the productivity of key forage species in various post-logging age classes. Regenerating stands (6–20 years old) had the uppermost ground vegetation cover, providing both the highest density and a high biomass of berries. Black bears preferred regenerating stands (6–20 years old) and avoided mature coniferous forests inside their home range. Home-range size was inversely related to the proportion of regenerating stands (6–20 years old). Intensive logging influenced black bear habitat and space use patterns, and presumably their population dynamics. An increase in the habitat quality of an opportunistic predator poses great concern for conservation, especially for forest-dwelling woodland caribou ( Rangifer tarandus caribou (Gmelin, 1788)).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".