Identifying Differences in Roadless Areas in Canada Based on Global, National, and Regional Road Datasets
Bibliographic record
Abstract
Roads are an overwhelming component of the global human footprint and their absence helps identify intact areas with high ecological value. Such areas are decreasing globally, making accurate estimation of their location and area of great importance. As the world’s second largest country, Canada contains a high proportion of the planet's remaining undisturbed areas. Identification of road-free regions requires accurate data, but substantial variability exists in road network datasets created and maintained at different spatial scales. We compared estimates of road length, density, and roadless areas across Canada, finding that global and national scale datasets included far fewer roads than regional-scale data or volunteered geographic information (VGI), with pronounced differences in less-developed areas. Regional-scale datasets had the lowest estimates of roadless area and smallest mean roadless patch size and are likely the most complete road maps, but are not available for all jurisdictions, limiting their national scale utility. VGI provides a national-scale alternative but still lacks many low-use roads. Available global and national datasets have insufficient information for accurate assessments of roadless areas in Canada, which will require detailed, consistent subnational datasets assembled and maintained by each province and territory in a coordinated fashion to achieve national coverage.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".