Remote permafrost terrain mapping, Grays Bay-Yellowknife corridor, Northwest Territories and Nunavut
Bibliographic record
Abstract
The new Grays Bay - Yellowknife corridor will provide transportation, energy, and telecommunications to northcentral Canada, and connect existing highway infrastructure to a deep-sea port on the Arctic Ocean. This infrastructure will improve access for exploration and development, and reduce operating costs in this mineral-rich region of the Northwest Territories and Nunavut, one of the biggest impediments to development in this region. Relatively little information on permafrost and ground ice conditions is available for most of the corridor. To fill this information gap, the Geological Survey of Canada's GEM-GeoNorth program is compiling permafrost and surficial geology information, mapping periglacial terrain features, and modelling ground ice conditions along the corridor. This poster presents the periglacial feature mapping component. Periglacial features are landforms associated with cold environments and typically contain permafrost. Permafrost, and its related ground ice, affect terrain sensitivity to climate change and surface disturbance. With development of this periglacial terrain and accelerated climate warming in the north, communities, planners, and regulators require this information to make informed decisions on how and where to build and manage environmentally sustainable and climate change-resilient infrastructure, and determine best adaptation strategies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".