Quantification of Multi-Use Trail Effects Using a Rangeland Health Monitoring Approach and Google Earth
Bibliographic record
Abstract
Creation and use of multi-use trails are increasing throughout grasslands of North America. While the direct and indirect ecological impacts of multi-use trails are generally understood, their specific impacts on adjacent grassland conservation require further assessment. Traditional scientific methods of quantifying trail impacts are often prohibitively costly in terms of required time, expertise, and equipment. Here, we evaluate the utility of a rapid assessment methodology—combining rangeland health protocols for grasslands with publicly available Google Earth mapping technologies—for capturing trail impacts as a function of distance from trail in a multi-use natural area in southwestern Alberta, Canada. Our methodology successfully detected a positive relationship between rangeland health scores and increasing distance from trail, indicating its viability as a rapid assessment tool. Second, this methodology was sensitive enough to allow the development of a more generalized statistical model demonstrating that rangeland health was best explained by a combination of slope, aspect, plant community type, and distance from trail. Combined, we suggest the limited costs of this method, combined with its ability to detect indirect impacts of trails on the health of adjacent grasslands, indicate this tool has potential utility for land managers where resources are limited. More specifically, we suggest this grassland health protocol can be highly effective as a first “rapid assessment,” prior to investing in more traditional ecological methodologies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".