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Record W4414901726 · doi:10.1080/09669582.2025.2563664

Minimizing backcountry overnight visitation impacts: a comparison of per capita resource impacts from lodges, huts, and campsites in Banff National Park and Mount Assiniboine Park, Canada

2025· article· en· W4414901726 on OpenAlexaboutno aff
Jeffrey L. Marion, Johanna Arredondo

Bibliographic record

VenueJournal of Sustainable Tourism · 2025
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsnot available
Fundersnot available
KeywordsNational parkMountPer capitaResource (disambiguation)TourismNature tourismMontane ecology

Abstract

fetched live from OpenAlex

A core sustainable tourism objective is to develop recreation infrastructures that accommodate visitation while minimizing the spatial “footprint” of resource impact. This recreation ecology study evaluated several measures of the areal extent of resource impact associated with overnight visitation in backcountry areas of two Canadian protected areas. Four types of accommodation were evaluated: a full-service lodge, self-service huts, developed campsites with formal tent pads, and primitive campsites. Innovative field-assessment protocols yielded measures of “facility footprint” and “visitor impact” to provide accurate assessments of per site and per person impact for each accommodation option and several specific facility types, including formal and informal trails. Huts provided the most effective option for accommodating overnight visitation with exceptionally little resource impact. For campsites, the spatial extent of impact is best minimized by including facilities that attract and spatially concentrate camping activities, and by using campsites in sloping, rocky, or uneven terrain and avoiding flat open terrain.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.185
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.291
Teacher spread0.284 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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