Systems Analysis of Climate Change Vulnerability for the US Northeast Ski Sector
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the greatest challenges to the sustainability of the winter tourism sector is climate change. Studies examining the implications of climate change for the ski tourism industry have mainly focused on vulnerability of the supply side (i.e. ski area infrastructure and operators) with limited attention given to the demand side (i.e. how tourists will respond to changing climate and ski conditions). A more holistic understanding of how the winter tourism marketplace may evolve under a changed climate is required for managers and communities to develop and plan specific adaptation strategies. Using a systems approach this study examines climate change vulnerability of both the supply and demand sides of the US Northeast ski tourism sector (i.e. a marketplace of some 103 ski areas across the states of New York, Vermont, New Hampshire, Maine, Massachusetts, Rhode Island and Connecticut). Multiple methods were employed including a climate change analogue (demand and supply side), future climate change and operations modeling (supply side), and a skier survey (demand side). Findings reveal a complexity of interacting and opposing impacts including the projected contraction northward of viable ski areas. In response to projected ski area closures in the region, demand for skiing opportunities is not likely to decrease proportionally. Ski areas that are able to remain operational under changed conditions should plan for a possible market-shift (i.e. spatial substitution) and may expect crowding issues and residual development pressure in association with the concentration of ski areas in fewer climate-advantaged regions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it