Permafrost degradation in the ice-wedge tundra terrace of Paulatuk Peninsula (Darnley Bay, Canada)
Bibliographic record
Abstract
The warming of high latitudes climate is enhancing the degradation of ground-ice and inducing important \nlandscape changes across the Arctic. This new Arctic state affects geomorphological dynamics, hydrology, and \necosystems, and poses challenges to the stability of infrastructure and livelihoods of Arctic communities. This \nstudy focuses on the hamlet of Paulatuk within the Inuvialuit Settlement Region of the Amundsen Gulf, south \nDarnley Bay, in northern Canada. In the summer of 2019, an ultra-high resolution aerial survey with a fixed-wing \nUnmanned Aerial Vehicle (UAV) was conducted, generating a 5 cm spatial resolution orthomosaic and Digital \nSurface Model (DSM). These, together with field observations were used to produce a very-high resolution \ngeomorphological map of the settlement and surrounding coastal areas. Landscape changes were analyzed using \nhistorical aerial imagery of 1975 and 1993, the 2019 UAV survey and a very-high resolution Pl´eiades satellite \nscene from 2020. The area is a tundra terrace made up of sandy fluvioglacial sediments affected by a dense \nnetwork of ice-wedge polygons, mostly high-centered, but also low-centered, showing signs of permafrost \ndegradation. Air and ground temperatures have increased respectively by 0.8 and 1.9 ◦C over last two decades at \nPaulatuk, and inter-polygon ponds surface increased by 23,000 m2 since 1975 due to ice-wedge thawing. The \nairstrip enhanced thaw pond formation on its margins, especially after 1993. The DSM reveals a depression south \nof the airstrip, which can be potentially flooded due to its proximity to the coastal waters.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".