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Record W2048537232 · doi:10.1080/11745398.2007.9686781

Climate Change Vulnerability of the Vermont Ski Tourism Industry (USA)

2007· article· en· W2048537232 on OpenAlexaff
Jackie Dawson, Daniel Scott

Bibliographic record

VenueAnnals of Leisure Research · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsClimate changeTourismVulnerability (computing)GeographyEcology

Abstract

fetched live from OpenAlex

Abstract Climate change represents one of the most significant challenges to humanity in the 21st century and is anticipated to have major consequences for the climate‐sensitive winter sports tourism sector. This study examined the vulnerability of Vermont's (US A) 18 ski areas. Under the six climate change scenarios examined, shortened ski seasons and increased snowmaking requirements were projected at all ski areas. Using two broad economic risk criteria (100‐day ski season, probability of being operational for the Christmas/New Year holiday period), only the two low‐elevation ski areas (Cochran and Suicide Six) were considered highly vulnerable to climate change. Consequently, it is not the entire Vermont ski tourism industry that is at risk to climate change but rather the individual low lying ski areas. The economic implications remain uncertain, but a possible outcome of a contraction of ski areas in the region is increased market share among the remaining ski areas.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.478
GPT teacher head0.545
Teacher spread0.067 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations54
Published2007
Admission routes1
Has abstractyes

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