A Comparison of All-Terrain Vehicle (ATV) Trail Impacts on Boreal Habitats Across Scales
Bibliographic record
Abstract
Recreational trails are an agent 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 their ecological integrity. Environments with low productivity may be particularly susceptible due to low resistance and resilience to recreational impacts. Our study examined 28 all-terrain vehicle (ATV) trails within the Avalon Wilderness Reserve and the adjacent surrounding area on the island of Newfoundland, Canada. We found 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. Bog sites had low resistance to both direct and indirect trail disturbance. There have been limited studies on ATV trail impacts in boreal environments, and our findings provide guidance for managers in such environments to manage recreational vehicle use.Nyssa van Vierssen Trip and Yolanda F. Wiersma. 2015. “A Comparison of All-Terrain Vehicle (ATV) Trail Impacts on Boreal Habitats Across Scales.” Natural Areas Journal 35(2):266-278.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".