Impacts of boat-based wildlife viewing in the K'tzim-a-deen Inlet on grizzly bear (Ursus arctos) behavior
Bibliographic record
Abstract
All forms of recreation and tourism, including wildlife viewing, have the potential to alter wildlife habitat, behavior, survival, and/or reproductive success. The increasing number of visitors pursuing bear-viewing activities in coastal British Columbia, Canada, and Alaska, United States, has led to a number of studies assessing the impact of wildlife viewing on bear behavior. This study, the first to assess the impact of boat-based bear viewing in this region, used focal sampling to measure bear activity budgets in the absence and presence of nonresearch bear-viewing vessels. We found that: (1) some grizzly bears were clearly tolerant of wildlife viewing activities while others were not; (2) individual variation of bears’ response to tourists was significant, introducing considerable uncertainty in attempting to assess medium- to long-term impacts of wildlife viewing; (3) males were rarely observed outside of the mating season, suggesting females (especially those with cubs) may use viewing areas as refuges from male grizzly bears; and (4) overt reaction distances varied greatly, suggesting that one appropriate management option may be to ensure boat captains can recognize potential displacement behavior in bears to avoid affecting subject animals. Some of the uncertainty arising from the biological research could be tempered by examining the social perspective of bear viewing tours to create an appropriate management plan for the K’tzim-a-deen Inlet Conservancy.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".