A regional comparison of the implications of climate change for the golf industry in Canada
Bibliographic record
Abstract
Golf is a recreation industry particularly sensitive to climate, yet the potential implications of climate change for the industry remain largely unexamined. This study presents findings of the first known impact assessment to compare the regional impacts of projected changes in the climate on the golf industry in Canada (or internationally). Empirical relationships between daily rounds played and four weather variables were defined through multiple regression analysis and then used to examine the potential impacts of two climate change scenarios on the length of the golf season and the number of rounds played in three regions of Canada (West Coast, Great Lakes, East Coast). Regionally, the West Coast region was projected to benefit the least from projected climate change, as golf courses that are currently open year round experienced only slight projected increases in rounds played in the 2020s and 2050s. Golf courses in the Great Lakes region could experience a 10‐ to 51‐day longer average golf season and a 21 percent to 3 percent increase in rounds as early as the 2020s, and an even more pronounced increase in the 2050s. East Coast golf courses were projected to benefit the most under both climate change scenarios, experiencing larger gains in average operating seasons (25 to 45 days in the 2020s) and a 40 percent to 48 percent increase in rounds played by as early as the 2020s.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".