Modelled ground ice conditions in the Kivalliq region, Nunavut, Canada
Bibliographic record
Abstract
Significant infrastructure development is proposed in the Kivalliq Region, Nunavut, including a 1,200 km long hydroelectric/fibre optic link to southern Canada.Knowledge of ground ice conditions is important to mitigate the effects of climate change and permafrost thaw on infrastructure.Nevertheless, only limited information exists in this region.The Geological Survey of Canada (GSC) has developed updated ground ice mapping for Canada and tested the modelling framework at regional scales using more detailed surficial geology mapping.The ground ice modelling depicts the estimated relative abundance of relict (buried glacial) ice, segregated ice, and wedge ice in the upper five metres of permafrost.Here we present modelling for the Kivalliq region based on standardized 1:125,000 scale surficial geology mapping.Such modelling is useful at a reconnaissance level and to guide more detailed ground ice investigations.Relict ice is predicted over limited areas above the postglacial marine limit.High segregated ice abundance occurs in fine-grained marine deposits within the limit of inundation.Modelled segregated ice abundance is medium or low in thicker till deposits.Wedge ice abundance is negligible or low due to the dominantly thin and coarse-grained surficial cover, and (relatively) limited time since subaerial exposure of the terrain following deglaciation near the coast.Overall, the highest ice contents are predicted above marine limit in thick, fine-grained till deposits.Areas with high relict ice abundance coincide with evidence of ice-cored terrain in satellite imagery, and modelled segregated ice is in general agreement with the limited field observations from the region. 1
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".