Sea Ice Classification from SWOT Observations: A Preliminary Analysis
Bibliographic record
Abstract
Sea ice mapping in the Canadian Arctic is essential for monitoring climate change impacts and supporting safe navigation. Synthetic Aperture Radar (SAR) missions such as RADARSAT Constellation Mission (RCM) and Sentinel-1 have proven effective for ice typing since they provide high spatial resolution and weather independence. Conversely, the Surface Water and Ocean Topography (SWOT) satellite offers complimentary altimetric measurements with the potential to provide two-dimensional surface elevation maps. This study proposes a machine learning approach for sea ice classification using Ka-band SAR imagery from the SWOT mission. We investigate the influence of SWOT’s radar incidence angle on classification performance. Several locations in the Canadian Arctic are selected as case studies. In addition, classification results are compared with sea ice types derived from co-located imagery from the RCM and Sentinel-1 satellites. Through this study, we aim to support the ice flags in SWOT products by incorporating information on different sea ice types.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".