Exploring bear attractant management strategies in Vancouver Island campgrounds
Bibliographic record
Abstract
Human-wildlife conflict is a global problem. As the human population increases, we further encroach on wildlife habitat. British Columbia has experienced high levels of human-wildlife conflict involving black bears. These can occur in nature tourism contexts such as campgrounds. Campgrounds are often constructed in or near bear habitat because of the beautiful natural appearing terrain. Not only do visitors enjoy natural settings, they also seek to experience wildlife in their own habitat. On Vancouver Island, campgrounds constructed in semi-urban or rural environments are not exempt from human-bear conflict. While bears adapt to the presence of humans, humans do not always adapt their behaviour when they are in wildlife habitat. Consequently, bears can become habituated to people and food-conditioned when they take advantage of unsecured human food and garbage sources. This puts bears at risk of destruction if they are seen as a danger to visitors. Campgrounds have a major role to play in mitigating human-bear conflict through proper management of bear attractants. This research explores black bear attractant management (BBAM) strategies in campgrounds on Vancouver Island, using a mixed methods research design. The findings indicated that all campgrounds employ BBAM strategies to some degree, but there were no consistent approaches found across the campgrounds. Most campgrounds in the study had highly attractive campsites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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 teacher head, 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".