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Characterizing and Comparing Backcountry Trail Conditions in Mount Robson Provincial Park, Canada

2007· article· en· W2177824022 on OpenAlexaffabout
Sanjay K. Nepal, Paul A. Way

Bibliographic record

VenueAMBIO · 2007
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsMountTransectGeographyNatural (archaeology)EcologyEnvironmental resource managementEnvironmental scienceArchaeologyEngineering

Abstract

fetched live from OpenAlex

Two backcountry trails located within the Mount Robson Provincial Park boundaries in British Columbia, Canada, are compared for the type of ecological characteristics and the influence of topographical use level and management on trail degradation. Data on five trail impact variables were collected at 68 fixed line transects, and information on management features, use level, and water-related problems were based on a survey of 31 km of trails. Results show that the two trails are similar in several ecological characteristics. The Berg Lake Trail (BLT), considered to be highly used and intensively managed, had more significant ecological problems than did Mount Fitzwilliam Trail (FWT), considered to be less highly used and intensively managed. However, ecological impacts on the FWT appear to be statistically no less different than on the BLT. It is concluded that effective trail management can mitigate many ecological problems that result due to the natural topographic conditions and use levels.

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 score0.016
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.279
Teacher spread0.261 · 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

Citations12
Published2007
Admission routes2
Has abstractyes

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