USING AN ANOLOGUE APPROACH TO EXAMINE CLIMATE CHANGE VULNERABILTY OF THE NEW ENGLAND (USA) SKI TOURISM INDUSTRY
Bibliographic record
Abstract
ABSTRACT Climate change is projected to have a significant impact on winter recreation-tourism sector including the multi-billion dollar ski tourism industry. Detrimental effects for the industry have been projected in numerous studies throughout several continents including, Australia, Asia, Europe and North America. Modeling based studies have revealed shortened ski seasons and increased snowmaking requirements under warmer temperatures. This study uses a climate change analogue approach to examine how a wider range of ski area performance indicators were affected by anomalously warm winters in the New England region of the USA. The record warm winter of 2001-02 is representative of projected future average winter climate conditions in the New England region under a high greenhouse gas emission scenario for the 2040-69 period and was used as one climate change analogue for this analysis. The 1998-99 ski season was also used as a climate change analogue as it represents the last of three consecutive warm winters that are representative of average winter conditions for the 2010-39 period. Ski area performance indicators for the 2001-02 and 1998-99 analogue years were compared to the climatically normal (for 1961-90) years of 2000-01 and 2004-05. The indicators examined include: ski season length, snowmaking (hours of operation and % fuel utilized as a proxy for fuel costs), total skier visits, visitation by time of year, average season passes sold, and operating profit ( % of total gross revenue and % of total gross fixed assets). The revealed impact on ski season length during these climate change analogue years is also compared with modeled impacts for the region.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".