Bear jams in Kananaskis country: Visitor and expert perceptions of roadside bear viewing management in Peter Lougheed Provincial Park, Alberta
Bibliographic record
Abstract
Bear viewing poses significant risks for humans and wildlife, particularly in the form of "bear jams" - traffic congestion caused by people slowing down or stopping to view bears. This study addresses gaps in our understanding of the human dimensions of roadside bear viewing, focusing on the case study of Peter Lougheed Provincial Park (PLPP) in Alberta, Canada. A mixed-methods approach is employed in this study, comprising a survey (n=380) of visitors who have witnessed or engaged in roadside bear viewing and interviews with experts who have experience in bear-related fields (n=22). The main findings indicate that a balanced approach is needed to manage bears and humans. The study suggests human, bear, infrastructure, and habitat-related approaches, including implementing no-stopping zones, enforcing regulations, and improving education and outreach, are key to managing roadside bear viewing. The results of this study provide valuable insight for park managers and wildlife officials to develop effective management strategies that balance the needs of both humans and bears. The data collected in this study underscore the importance of a holistic and adaptive management approach to addressing roadside bear viewing. The findings apply to PLPP and parks facing similar challenges
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".