‘It’s a people problem, not a goat problem.’ Mitigating human–mountain goat interactions in a Canadian Provincial Park
Bibliographic record
Abstract
Context Wildlife viewing is a primary reason people visit parks and protected areas. However, high rates of visitation increase the potential for interactions between humans and wildlife. This close proximity of humans and wildlife can lead to habituation to human presence and pose a threat to both animals and humans. Aims We describe human–mountain goat interactions in Cathedral Provincial Park (CPP), in British Columbia (BC), Canada, and examine management and mitigation strategies to reduce these interactions. Methods This project was a collaboration with BC Parks. We used community-based participatory research methodologies, conducting interviews and surveys from July 2020 to November 2021 with park visitors, staff, and researchers. Key results Most respondents encountered mountain goats in the park and understood the park’s messaging; however, not all respondents took the necessary steps to reduce encounters. We recommend further education efforts focused on formal staff training and improved infrastructure in the park. Conclusions Our results can be used to inform management decisions related to human–wildlife interactions, primarily in parks and protected areas. On a proximate level, we suggest further educational efforts and improved infrastructure in the park to help overcome perceived lack of action by some participants. Ultimately, there is a need to incorporate human aspects of human–wildlife interactions into management decisions aimed at addressing potential and existing problems. Implications Using a multitude of approaches to management, informed by biological, social, and cultural knowledge, can improve responses and mitigation strategies in human–wildlife interactions. Collaboration among different stakeholders allows for the exchange of ideas and innovations that can contribute to positive movement towards coexistence of humans and wildlife in parks and recreational areas.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".