Sea Ice Elevation in the Western Weddell Sea, Antarctica: Observations From Field Campaign
Bibliographic record
Abstract
Abstract Sea ice elevation is crucial in the characterization of three‐dimensional (3D) sea ice patterns, providing physical insights to advance sea ice dynamic models. Moreover, how sea ice elevation may be related to the ocean geophysical environment is still a significant knowledge gap, especially in Antarctica. A radar theory relating electromagnetic scattering mechanisms to sea ice elevation over old and deformed rough ice has been reported in a prior companion paper. This follow‐up paper presents the validated model function and synthetic aperture radar (SAR)‐retrieved sea ice elevations based on the field data acquired during the Operation IceBridge and TanDEM‐X Antarctic Science Campaign. A high‐resolution sea ice digital elevation model (DEM) is generated extensively over a 19 × 450 km sector in the Western Weddell Sea, achieving a good accuracy with a low root‐mean‐square error of 0.23 m. From the SAR‐retrieved sea ice DEM, 3D sea ice patterns including roughness height, auto‐correlation lengths, correlation ellipticity, and orientation angles are calculated over the old and deformed rough sea ice. The 3D sea ice patterns give a comprehensive characterization of sea ice topography in the Western Weddell Sea and show the potential to be used for understanding sea ice formation processes in the Antarctic.
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) | 0.000 | 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 | 0.000 |
| 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".