Observations of Thin First Year Sea Ice Using a Suite of Surface Radar, LiDAR, and Drone Sensors
Bibliographic record
Abstract
Arctic sea ice is rapidly transitioning into a perennial first year ice pack and this is being observed with satellite remote sensing. Satellite image interpretation requires accurate knowledge of the physical conditions and how they give rise to the microwave scattering response that is present within a single image pixel. This study addresses this issue through a focused remote sensing study of thin first year sea ice. We present results from an experiment that fused datasets from surface-based C- and Ku-band polarimetric scatterometers, LiDAR, and drone-based optical and thermal infrared sensors. We grew frost-flower-covered thin first year sea ice in a mesocosm facility and measured the time-series evolution of C- and Ku-band scattering response as it evolved into snow-covered sea ice. Drone surveys, LiDAR scans, and physical sampling provided complementary characterization of the ice. Results quantify the sensitivity of C- and Ku-band to the presence of frost flowers, the addition of snow, and the meteorological conditions. Drone surveys enhanced the characterization by rapidly performing observations over a larger representative region. In essence, they are helping to close the gap between surface-based sensing and satellite imagery. Furthermore, this study complements and enhances our understanding of the snow-covered sea ice system.
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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".