How climate change and population growth will shape attendance and human-wildlife interactions at British Columbia parks
Bibliographic record
Abstract
Protected areas are important for ecological conservation while simultaneously supporting culturally and economically valuable tourism.However, excessive visitor pressure strain operations and risk human-wildlife conflict, threatening the sustainability of nature-based tourism.Thus, park managers need to know what factors underpin attendance and how these might interact to shape future attendance.Using a decade of attendance records from 249 provincial parks in British Columbia (BC), Canada, as well as 12 years of human-wildlife interactions (HWI) records at five national parks in BC, we modelled the impacts of weather conditions and population growth on park attendance and HWIs.We paired these models with climate change and population growth scenarios to generate projections of how attendance and HWIs will change throughout the century.Climate change is projected to result in more precipitation and higher temperatures, and, over this same time span, BC's population is expected to grow substantially.Based on the observed relationship between attendance and weather, parks should anticipate a marked rise in visitors and HWIs, especially during their respective peak seasons.These projections provide park managers with the information required for proactive management, ultimately contributing to the sustainability of recreation and tourism in protected 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".