RADARSAT SAR Sea Ice Imagery. Version 1.0
Bibliographic record
Abstract
The Canadian RADARSAT satellite collected 195 synthetic aperture radar (SAR) images of the SHEBA site between November 1, 1997, and October 8, 1998 (roughly one image every 3-5 days). The C-band (5.3 GHz) RADARSAT SAR imaged a swath on the earth 460 km wide (in ScanSAR mode) with a pixel size of 50 m, unhampered by clouds or darkness. The satellite data were received and processed into imagery at the Alaska SAR Facility (ASF) in Fairbanks. Sequential pairs of images were then processed by the RADARSAT Geophysical Processor System (RGPS) at the Jet Propulsion Laboratory (JPL) in Pasadena to derive the motion of the sea ice on a 5-km grid by tracking common features in each pair of images. Thus we have a year-long record of the spatial pattern of ice motion and the radar backscatter in the vicinity of the SHEBA site. There are 195 images from which two sub-images were extracted, with the SHEBA station at the center of each. One sub-image was at the original spatial resolution of 50 meters per pixel, and 800 x 800 pixels (40 x 40 km). The other sub-image was spatially averaged to 250 meters per pixel, and was also 800 x 800 pixels (200 x 200 km). The two sets of 195 sub-images are bundled into two .tar files. Additionally, there is an animated GIF file of each sequence of 195 sub-images.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.013 |
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".