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Record W349064987

A comparison of all-terrain vehicle (ATV) trail impacts in boreal forest, heath and bog habitats within the Avalon Wilderness Reserve and surrounding area

2014· dissertation· en· W349064987 on OpenAlexaboutno aff
Nyssa van Vierssen Trip

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2014
Typedissertation
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsnot available
Fundersnot available
KeywordsDisturbance (geology)Wilderness areaVegetation (pathology)RecreationBorealResistance (ecology)WildernessGeographyBogHabitatEnvironmental scienceNature reserveEcologyLoggingForestryPeatGeology
DOInot available

Abstract

fetched live from OpenAlex

Recreational trails are a source of anthropogenic disturbance in nature reserves and other low human impact areas. Effective management must balance the desire of recreationists to use these natural areas with the need to maintain the ecological integrity of these areas. Low productivity environments may be particularly susceptible due to low resilience to recreational impacts. My study examined 28 all-terrain vehicle (ATV) trails within the Avalon Wilderness Reserve and the adjacent surrounding area in Newfoundland, Canada. My research showed that different habitat types (boreal forest, heaths and bogs) differ in resistance and resilience to both direct on-trail erosion and indirect off-trail vegetation impacts of ATV trails. Dry forested sites were more resistant to direct on-trail erosion but less resistant to indirect off-trail vegetation disturbance. Heath sites were less resistant to direct on-trail erosion but highly resistant to indirect off-trail disturbance. Bogs sites had low resistance to both direct and indirect trail disturbance. There have been limited studies on ATV trail impacts in boreal environments, and these findings provide guidance for managers in Newfoundland and Labrador to manage recreational vehicle use.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.057
GPT teacher head0.340
Teacher spread0.282 · 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.

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
Published2014
Admission routes1
Has abstractyes

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