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Record W2057499871 · doi:10.1657/1938-4246-44.3.277

Effects of Recreational Traffic on Alpine Plant Communities in the Northern Canadian Rockies

2012· article· en· W2057499871 on OpenAlexaffabout
Varina E. Crisfield, S. Ellen Macdonald, A. Joyce Gould

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2012
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsAlberta Environment and Protected AreasUniversity of Alberta
Fundersnot available
KeywordsTramplingTundraEnvironmental sciencePlant communityEcologySpecies richnessDisturbance (geology)EcosystemVascular plantAlpine plantLichenRecreationSoil compactionGeographyPhysical geographySoil waterGrazingBiologySoil science

Abstract

fetched live from OpenAlex

Recreational activities in alpine areas have been increasing in recent decades, creating the need to improve our understanding of the impacts of these activities and how they are best managed. We explored impacts of recreational trail use on dry alpine meadows in the northern Canadian Rockies of Alberta. Data collected in 142 plots (0.5 m × 1 m) were used to compare plant community metrics among (1) a recreational trail, (2) intact tundra meadows (undisturbed), and (3) sparsely vegetated gravel steps formed by frost disturbance (naturally disturbed). As compared to undisturbed tundra, trails had substantially lower cover of vascular plants (4% vs. 35%), lichen (0% vs. 10%), and cryptogamic crust (0% vs. 4%); trails also had lower species richness (7 vs. 11 species per plot), but greater soil compaction (2.75 vs. 1.25 kg cm-2). Trails differed from natural gravel steps, which had three times more biotic cover and different composition. This highlights the difference in effects of human and natural disturbance. Positive feedback effects of trampling in tundra ecosystems may lead to altered environmental conditions, including decreased infiltration capacity and nutrient cycles in soils, and more extreme temperatures at the soil surface. These feedbacks could inhibit regeneration of abandoned trails.

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)0.002
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 score0.145
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.063
GPT teacher head0.350
Teacher spread0.287 · 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

Citations31
Published2012
Admission routes2
Has abstractyes

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