Identifying differences in roadless areas in Canada based on global, national, and regional road datasets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Roads are an overwhelming component of the global human footprint and their absence helps identify intact areas with high ecological value. Road‐free areas are decreasing globally, making accurate estimation of their location and size of great importance. Identification of such 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, which contains a high proportion of the world's remaining undisturbed and road‐free areas. Global‐ and national‐scale datasets included, on average, only 11%–14% of roads represented in regional‐scale data or volunteered geographic information (VGI), with the most pronounced differences in less‐developed areas. Regional‐scale datasets, with the lowest estimates of amount of roadless area and smallest mean roadless patch size, are likely the most complete road datasets 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.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it