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Record W2601737973

Weather and Camping in Ontario Parks

2012· dissertation· en· W2601737973 on OpenAlexaboutno aff
Micah J. Hewer

Bibliographic record

VenueUWSpace (University of Waterloo) · 2012
Typedissertation
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyMeteorologyEnvironmental scienceForestry
DOInot available

Abstract

fetched live from OpenAlex

Climate and weather have a major influence over seasonality in nature-based tourism by determining the availability and quality of certain outdoor recreational activities (Butler, 2001). Climate and weather act as central motivators involved in the travel decisions of tourists (Mintel International Group, 1991; Kozak, 2002). Climate as an attraction is also an integral component considered in destination choice among tourists (Lohmann and Kaim, 1999; Hamilton and Lau, 2005; Gössling et al. 2006). Due to the relationship between climate, weather and outdoor recreation, climate change is expected to have a direct impact on park visitation and camper decision-making (Jones and Scott, 2006a; 2006b). This study contributes to the understanding of weather sensitivity for different tourism segments across varying climate zones world-wide which can contribute to more informed park tourism planning and climate change adaptation in Ontario. Using a survey-based approach, this study identified and compared the stated weather preferences and weather related decision-making of campers from two different provincial parks in Ontario. The two provincial parks selected as case studies, based on differing park characteristics and perceived climatic requirements, were Pinery and Grundy Lake. \n \nStatistically significant differences (at the 95 percent confidence level) were observed in stated weather preferences and weather related decision-making, based on differences in respondent characteristics. Most notably, activity participation, length of planned stay and age of the respondent had the most significant and widespread effect on weather preferences and camper decision-making. Temperature preferences between the two parks were strikingly similar. However, differences in weather related decision-making were statistically significant showing campers at Pinery to be more sensitive to weather than those at Grundy Lake. Overall, parks that are more beach-oriented, closer to tourism generating areas and are characterised by visitors with shorter than average lengths of stay, are likely to be the most sensitive to weather variability. As such, it will be most important for parks that rely on similar tourism generating markets and share similar park characteristics as Pinery, to place a greater planning emphasis on climate change adaptation, as these parks are likely to be most affected by the impact of climate change on park visitation in Ontario. \n \nClimatic warming was not perceived by campers as a major threat to park visitation in Ontario. Instead, heavy rain, strong winds and unacceptably cool temperatures were the most influential weather variables in relation to camper decision-making. In response to the perceived threat of heavy rain and strong winds to camping in Ontario, and in association with projected increases pertaining to the frequency and intensity of these weather events under climate change, a number of recommendations have been made, which could be implemented by Ontario Parks in an effort to reduce camper vulnerability to extreme weather and improve overall trip satisfaction.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.057
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.192
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2012
Admission routes1
Has abstractyes

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