Climate Change and Tourism in the Great Lakes Region: A Summary of Risks and Opportunities
Bibliographic record
Abstract
An integral component of the tourism/recreation sector in the Great Lakes region of Canada is climate. Climate defines the length and quality of tourism seasons and associated levels of participation (i.e., natural seasonality) and it affects the natural resource base that many forms of tourism depend upon. Changes in natural seasonality and the environment induced by climate change could have substantial implications for the sustainability of specific tourism sectors and the communities that depend on them. This article summarizes existing literature to provide an overview of the risks and opportunities climate change poses for the tourism/recreation sector across the entire Great Lakes region. Winter tourism is projected to be negatively impacted in the region, with reductions in season length for skiing, snowmobiling, and ice fishing. Warm weather tourism is projected to benefit from climate change through extended seasons for major activities such as golfing, park visitation, camping, beach use, and boating. The differential effects of climate change in the Great Lakes region will alter the competiveness of tourism sectors. Determining how tourism operators and communities will need to adapt to supply- and demand-side changes in order to reduce the risk and take advantage of new opportunities in a sustainable manner remains an important area for future inquiry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".