Seasonal Surface Subsidence and Frost Heave Detected by C-Band DInSAR in a High Arctic Environment, Cape Bounty, Melville Island, Nunavut, Canada
Bibliographic record
Abstract
Differential interferometry of synthetic aperture radar (DInSAR) can be used to generate high-precision surface displacement maps in continuous permafrost environments, capturing isotropic surface subsidence and uplift associated with the seasonal freeze and thaw cycle. We generated seasonal displacement maps using DInSAR with ultrafine-beam Radarsat-2 data for the summers of 2013, 2015, and 2019 at Cape Bounty, Melville Island, and examined them in combination with a land-cover classification, meteorological data, topographic data, optical satellite imagery, and in situ measures of soil moisture, soil temperature, and depth to the frost table. Over the three years studied, displacement magnitudes (estimated uncertainty ± 1 cm) of up to 10 cm per 48-day DInSAR stack were detected. However, generally, the displacement was far smaller (up to 4 cm). Surface displacement was found to be most extensive and of the greatest magnitude in low-lying, wet, and steeply sloping areas. The few areas where large vertical displacements (>2.5 cm) were detected in multiple years were clustered in wet, low lying areas, on steep slopes or ridges, or close to the coast. DInSAR also captured the expansion of two medium-sized retrogressive thaw slumps (RTS), exhibiting widespread negative surface change in the slump floor.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".