A New Protocol to Map Permafrost Geomorphic Features and Advance Thaw-Susceptibility Modelling
Bibliographic record
Abstract
Permafrost thaw can destabilize terrain, initiate thermokarst processes that alter landscapes, and create geohazards for communities and infrastructure. A robust, standardized methodology was developed to map indicators of thaw-sensitive permafrost terrain, including mass wasting and periglacial features. The method was applied to a 10-km wide corridor centred on the Dempster and Inuvik-Tuktoyaktuk Highways, which are constructed over a wide range of terrain and permafrost conditions. Here we use random forest models, trained and validated with mass movement and ice-wedge polygon inventories, to develop thaw-susceptibility models for two regions along the corridor, Peel Plateau, and Anderson Plain and Tuktoyaktuk Coastlands. Geomorphological and hydrological variables were used as predictors providing insights into the characteristics constraining the distribution of thaw-sensitive terrain. In the Peel region, mass movements have a higher potential of occurring on concave, moderate to steep slopes (7 to 18°) in fluvially-incised valleys. In uplands of Anderson Plain and Tuktoyaktuk Coastlands, mass movements occur on moderate slopes (5 to 15°) adjacent to incised stream channels, and along lakeshores. The ice-wedge polygon model across the forest tundra transition north of Inuvik highlights the northward increase in polygonal terrain with decreasing ground temperatures.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.016 |
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".