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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".