Perceptions of Ecological Risk Associated with Mountain Pine Beetle (<i>Dendroctonus ponderosae</i>) Infestations in Banff and Kootenay National Parks of Canada
Bibliographic record
Abstract
Western Canada is experiencing an unprecedented outbreak of the mountain pine beetle (MPB). The MPB has the potential to impact some of Canada's national parks by affecting park ecosystems and the visitor experience. Controls have been initiated in some parks to lessen the impacts and to prevent the beetle from spreading beyond park boundaries. We examine the perception of ecological risk associated with MPB in two of Canada's national parks, the factors affecting perceptions of risk, and the influence of risk judgments on support for controlling MPB outbreaks in national parks. Data were collected using two studies of park visitors: a mail survey in 2003 and an onsite survey in 2005. The MPB was rated as posing a greater risk to the health and productivity of park ecosystems than anthropogenic hazards and other natural disturbance agents. Visitors who were familiar with MPB rated the ecological and visitor experience impacts as negative, unacceptable, and eliciting negative emotion. Knowledge and residency were the most consistent predictors of risk judgments. Of knowledge, risk, and demographic variables, only sex and risk to ecosystem domains influenced support for controlling the MPB in national parks. Implications for managing MPB in national parks, visitor education, and ecological integrity are discussed.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".