Short-Time Ice Drift and Deformation Measurements Using Multi-Mission Synethetic Aperture Radar
Bibliographic record
Abstract
Norway is in a good position regarding frequent access to synthetic aperture radar data. A Norwegian–\nCanadian agreement provides large quotas of RADARSAT-2 images used operationally by e.g. the Norwegian\nIce Service. More recently, Norway’s participation in the Copernicus program also allows rapid\naccess to SENTINEL-1A data. By combining these data sources we can get satellite time series with\nvery low time separation which allows us to study ice drift, deformation and ice growth processes with a\ntime resolution of minutes and hours rather than days. This allows us to measure the drift of fast moving\nice which is usually not observable due to insufficient time sampling. We study ice drift derived using\nvarying time separations ranging from minutes to one day and show the effect on estimated ice speeds.\nThe derived drift is used to construct deformation maps showing areas of converging and diverging ice.\nWe show that very high time resolution is sometimes necessary for measuring fast moving ice. We focus\non a particular example that illustrates how low time sampling implies missing significant changes in\ndeformation within one day
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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 | 0.001 |
| 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".