Co-polarized Synthetic Aperture Radar (SAR) Change Map between 11/28/2025 and 12/09/2025, Hubbard Glacier Earthquake
Bibliographic record
Abstract
This data release contains a radar-based remote sensing product used to detect regions of surface change in southern Yukon Territory, Canada and southeast Alaska, United States between November 28, 2025 and December 9, 2025, based on the availability of Sentinel-1 satellite data. Much of this surface change is interpreted to have been triggered by the M7.0 Hubbard Glacier Earthquake that occurred on December 6, 2025. This dataset was used to map probable landslides based on the increase of surface roughness that occurred between acquisitions (Allstadt and others, 2025). The co-polarized amplitude difference (vv_diff_dB_20251128_20251209) measures the change in strength of the synthetic aperture radar (SAR) backscatter signal between acquisitions. SAR amplitude data were downloaded from the Sentinel-1 Ground Range Detection product collection from the Copernicus Data Space Ecosystem (available at https://dataspace.copernicus.eu/). The data are from Sentinel-1 satellites' ascending track 50. This collection is provided in the dB scale, multi-looked to 10x10 m/pixel, and geocoded. I selected the co-polarized data for each acquisition and differenced the acquisitions to produce the data product. The data product shows all surface change between acquisitions and regions of poor data quality have not been masked. Some reasons for surface change in a snowy, mountainous environment include landslides, snow avalanches, new snowfall, changes in surface moisture, glacier movement and more (e.g., Rott and Mätzler, 1987; Lindsay and others, 2025). Reasons for poor data quality might include foreshortening and shadowing due to steep topography and the look angle of the satellite.
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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 | 0.002 | 0.002 |
| 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) | 0.004 | 0.002 |
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".