Image reconstruction of Arctic sea ice using SWIM data at small incidence angles
Bibliographic record
Abstract
The Surface Wave Investigation and Monitoring (SWIM) instrument is the first to adopt a rotation detection mode at multiple small incidence angles (0°-10°), which is mainly used for wave detection. SWIM is able to cover the north and south latitudes to 83°, and detect sea ice. However, the SWIM spatial resolution is relatively low (18 km), which limits its application in sea ice detection. Therefore, this study proposes, for the first time, a sea ice image reconstruction method at small incidence angles, which can improve the spatial resolution and extend the incidence angle range of image reconstruction methods. First, several image reconstruction methods are investigated and compared using SWIM data. Second, the optimal method (Scatterometer Image Reconstruction, SIR) with more sea ice detail and stronger noise suppression is selected. Third, the parameter settings of SIR are investigated and its reconstruction quality improves with increasing iteration number. Finally, based on the 6°-10° SWIM data in the Severnaya Zemlya and northeastern Kara Sea, the image reconstruction is carried out using the SIR method. The results are evaluated using the normalized standard deviation that can reach 0.55, and discussed based on sea ice charts and Sentinel-1 SAR images with good agreement.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".