Feasibility Analysis of Retrieving Sea Ice Surface and Bottom Roughness and Thickness from Polarimetric SAR
Bibliographic record
Abstract
Synthetic Aperture Radar (SAR) plays an important role in the refined inversion of sea ice surface, bottom roughness and sea ice thickness. At present, the research on SAR and sea ice morphology is not deep enough, and there is a lack of correlation research on the surface and bottom roughness of sea ice. In response to this problem, this paper uses the sea ice data provided by the Department of Fisheries and Oceans Canada (DFO) and the RadarSat-2 data to analyze the correlation between SAR features and sea ice surface roughness, as well as sea ice surface and bottom roughness. The results show that there is a certain correlation between SAR features and sea ice surface roughness, and the maximum absolute value of the correlation coefficient is 0.764. The roughness of sea ice surface and bottom has a strong correlation, which provides a theoretical basis for inversion of sea ice bottom roughness by SAR. In addition, SAR has great potential in estimating the thickness of deformed 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".