Measuring Spatial and Temporal Gravelled Forest Road Degradation in the Boreal Forest
Bibliographic record
Abstract
Degradation of forest roads in Canada has strong negative effects on access to forestlands, together with economic (e.g., increased maintenance costs), environmental (e.g., erosion of materials and subsequent habitat contamination), and social (e.g., use risks) impacts. Maintaining sustainable and safe access to forestland requires a better understanding and knowledge of forest road degradation over time and space. Our study aimed to identify relevant spatiotemporal variables regarding the state of eastern Canadian forest road networks by (1) building predictive models of gravel forest road degradation and assessing effects of the slope, time, loss of the road surface, and road width (field approach), and (2) evaluating the potential of topography, roughness and vegetation indices obtained from Airborne Laser Scanning (ALS) data and Sentinel-2 optical images to estimate degradation rates (remote sensing approach). The field approach (n = 207 sample plots) confirmed that only four variables were efficient to estimate degradation rates (pseudo-R2 = 0.43 with ±8% error). Simulations that were conducted showed that after about five years without maintenance, the rate of degradation on a road, regardless of its width, increased exponentially, exacerbated by a high slope gradient and loss of road surface. The narrowest roads tended to degrade more rapidly over time. The remote sensing approach performed quite well (pseudo-R2 = 0.34 with ±9% error) in terms of predicting road degradation, giving us the valuable tools to spatialise the state of gravel forest road degradation in eastern Canadian forest. This study provided new knowledge and tools that are critical for maintaining and sustaining access to Canada’s boreal forest territory in both the short- and the long-term.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".