Evaluation of new spaceborne SAR sensors for sea-ice monitoring in the Baltic Sea
Bibliographic record
Abstract
In this study, synthetic aperture radar (SAR) data from the Advanced Land Observing Satellite (ALOS) and the Envisat, RADARSAT-2, and TerraSAR-X satellites were compared to evaluate their usefulness for sea-ice monitoring in the Baltic Sea. Radar signature characteristics at different frequencies, polarizations, and spatial resolutions are presented for three examples from 2009. C-band like-polarization data, which have been used for operational sea-ice mapping since the early 1990s, serve as a reference. Advantages and disadvantages were identified for the different SAR systems and imaging modes. One conclusion is that cross-polarized data improve the discrimination between sea ice and open water. Another observation is that it is easier to identify ice ridges in L-band data than in images from shorter wavelengths. The information content of X- and C-band images is largely equivalent, whereas L-band data provide complementary information. L-band SAR also seems to be less sensitive to wet snow cover on the ice.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".