Geomorphic feature inventory along the Dempster and Inuvik to Tuktoyaktuk highway corridor, Yukon and Northwest Territories
Bibliographic record
Abstract
Thaw of permafrost and associated ground ice melt can reduce ground stability, modify terrain, and reconfigure drainage patterns affecting terrestrial and aquatic ecosystems and presenting challenges to northern infrastructure and societies. The integrity of ground-based transportation infrastructure is critical to northern communities. Geomorphic features can indicate ground ice presence and thaw susceptibility. This Geological Survey of Canada Open File presents the digital georeferenced database of landforms identified in continuous permafrost terrain using high-resolution satellite imagery. The database is for a 10 km-wide corridor centered on the Dempster and Inuvik-Tuktoyaktuk highways. This 875 km-long transect traverses a variety of geological and physiographic terrain types, including glaciated and non-glaciated terrain, in the northcentral Yukon and northwestern Northwest Territories, where variation in climate, relief, ecology, and disturbance have produced a variety of periglacial conditions. We identified geomorphic features in high-resolution (0.6 m) satellite imagery visualized in 3D, and digitized them in ArcGIS. We used custom Python scripts to populate the attributes for each geomorphic feature. A total of 8746 features were mapped by type and categorized within three main classes: hydrological (n = 1188), mass movement (n = 2435), and periglacial (n = 5123). Features were identified at 1:10 000 and mapped at 1:5000. This report presents the geospatial database in ESRI shapefile, Keyhole Markup Language (KML), and comma-delineated formats.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.007 | 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".