Synthetic aperture radar backscatter is influenced by bubbles at the ice/water interface of an Antarctic lake
Bibliographic record
Abstract
Abstract Synthetic aperture radar is an invaluable tool for monitoring lake ice. This study utilizes synthetic aperture radar to analyze the 2019-2023 backscatter time series of perennially ice-covered Lake Untersee in East Antarctica. We observed stark seasonal backscatter variations, averaging –9.6 dB from December to March and –3.7 dB from May to November. These fluctuations correspond to the abundance of sub-centimeter bubbles at the ice/water interface. Notably, the backscatter increase in April-May aligns closely with variations in ice thickness across the lake. Our findings suggest that ice cover thickness influences the timing and duration of ice accretion at the bottom, the accumulation of dissolved gases and bubbles, and the resultant changes in surface roughness at the ice/water interface. These factors collectively impact the backscatter response. This study enhances our understanding of the interactions between subsurface processes and synthetic aperture radar backscatter, shedding light on the seasonal dynamics of perennially ice-covered lakes.
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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 | 0.000 | 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) | 0.001 | 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".