Ku‐ and Ka‐Band Polarimetric Radar Waveforms and Snow Depth Estimation Over Multi‐Year Antarctic Sea Ice in the Weddell Sea
Bibliographic record
Abstract
Abstract Antarctic sea ice has seen recent rapid declines in extent, but it remains unclear whether this is accompanied by thinning. Due to the relative abundance and complexity of overlying snow on sea ice, radar altimetry methods routinely deployed for sea ice thickness estimation in the Arctic are difficult to apply in Antarctica. We present nadir‐looking radar waveforms from the first deployment of the KuKa surface‐based radar on Antarctic sea ice, specifically multi‐year sea ice in the Weddell Sea marginal ice zone with a thick snow cover. Coincident snow pits revealed thick layers of snow which were exposed to the summer melt season and superimposed ice. Our instrument detects only very small amount of co‐polarized radar backscatter from the sea ice surface, suggesting that conventional satellite altimeters may not always range to this interface. However, polarimetric snow depth determination performs well, with of 0.76 between measured and KuKa‐estimated snow depths.
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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 | 0.000 |
| 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 | 0.000 | 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 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".