Implications of climate change for visitation to ontario's provincial parks
Bibliographic record
Abstract
Parkprofessionals have recognized that global climate change could have significant implications for park conservation policy and management, but assessment of the implications for nature‐based tourism remains very limited. In the Province of Ontario, provincial parks are a major resource for nature‐based tourism, with more than 10 million person visits in 2003. This paper presents an empirical assessment of the potential impact of a changed climate on visitation in Ontario's provincial parks. Multiple regression analysis was used to develop a relationship between monthly park visits and climate for six high‐visitation parks selected to represent each of Ontario Park's administrative regions. The models were then used to examine the potential direct impact of changes in climate on the total annual number of visitors and the seasonal pattern of visitation to Ontario's parks using climate change scenarios for the 2020s, 2050s and 2080s. Visitation was projected to increase between 11% and 27% system‐wide in the 2020s and between 15% and 56% in the 2050s. When climate change was combined with the potential effects of demographic change, annual visits for the mid‐2020s were projected to be even higher than that projected under climate change alone (23% to 41%). Management implications of the projected visitation increases are also 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".