Spatial analysis of summer subsidence in Tuktoyaktuk Peninsula (2019 – 2024): linking Sentinel-1 D-InSAR and in-situ observations
Bibliographic record
Abstract
Climate warming is driving widespread changes in Arctic permafrost coasts, which comprise about 1/3 of global coastal areas. The Tuktoyaktuk Peninsula registered average shoreline retreat rates of -0.77 m/yr from 1950 to 2020, with a 31% increase since 1985. Coastal morphology significantly controlled shoreline evolution trends, with low-lying tundra flats retreating at higher rates. Permafrost-thaw subsidence, coastal erosion, and sea level rise are driving high land loss rates in low-lying permafrost areas. This study uses field and remote sensing methods to assess the factors contributing to permafrost subsidence and degradation on regional and local scales. UAS surveys were conducted in 2023 and 2024 in target low-lying coastal sites along the Tuktoyaktuk Peninsula such as Reindeer Point, Toker Point, Tuft Point and Warren Point. The UAS DSMs were used to derive very high-resolution digital surface models and to quantify short-term changes in target low-lying coastal sites. Sentinel-1-based D-InSAR analysis for the summers of 2019 to 2024 allowed for assessing regional scale and local surface deformations. We analysed the spatial variability of each summer and the interannual differences between them. Results show prevailing regional subsidence in all summers, with the highest values in 2021, where shoreline retreat hotspots such as Tuktoyaktuk Island, Toker Point, and Warren Point displayed more than 40 cm of subsidence. Warren Point registered the highest surface deformation patterns of the UAV surveyed areas between 2023 and 2024, likely due to coastal inundation events that degraded the permafrost and caused thaw subsidence.
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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 | 0.000 | 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) | 0.001 | 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".