Black bear (Ursus americanus) habitat ecology as related to aspects of forest management in southern New Brunswick
Bibliographic record
Abstract
To examine aspects of forest management, black bear ecology was studied in a protected area surrounded by intensively harvested and managed habitats. A multiresource survey was performed and bear space use and habitat selection behaviours were analysed. Through cartographic modelling, results were integrated to determine if unsuitable habitats generated a fragmentation effect. Ten adult female black bears were tracked from 1993 to 1994 and yielded 610 positions. Their large home ranges (x_ = 62.0 km2; SD = 34.3 km2 [95%MCP]) suggested a low quality landscape. Bears used protected and exploited portions as expected by chance. Hiding cover distance, horizontal cover density, canopy cover, safety tree density, food species ground cover, food species richness and food species diversity were surveyed at 113 sites in 14 habitat types. Habitat types were thus grouped graphically to perform habitat component selection analyses. Habitat type and component selection behaviour was explored through use versus availability testing. Landscape component (distance to water and area to perimeter ratio of habitat polygons) selection was assessed. Behaviour towards roads could not be assessed due to detectability issues. (Abstract shortened by UMI.)
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 | 0.000 |
| 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 | 0.000 |
| 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".