Toward the Discrimination of Oil Spills in Newly Formed Sea Ice Using C-Band Radar Polarimetric Parameters
Bibliographic record
Abstract
Climate-driven sea ice loss has exposed the Arctic to increased human activity, which comes along with a higher risk of oil spills. As a result, we investigated the ability of C-band polarimetric parameters in a controlled mesocosm to accurately identify and discriminate between oil-contaminated and uncontaminated newly formed sea ice (NI). Parameters, such as total power, copolarization ratio, copolarization correlation coefficient, and others, were derived from the normalized radar cross section and covariance matrix to characterize the temporal evolution of NI before and after oil spill events. For separation purposes, entropy ($H$) and mean-alpha ($\alpha$) were extracted from eigen decomposition of the coherency matrix. The$H$versus$\alpha $scatterplot revealed that a threshold classifier of 0.3-$H$and 18°-$\alpha $could distinguish oil-contaminated NI from its oil-free surroundings. From the temporal evolution of the polarimetric parameters, the results demonstrate that the copolarization correlation coefficient is the most reliable polarimetric parameter for oil spill detection, as it provides information on a variety of oil spill scenarios, including oil encapsulated within ice and oil spreading on top of ice. Overall, these findings will be used to support existing and future C-band polarimetric radar satellites for resolving ambiguities associated with Arctic oil spill events, particularly during freeze-up seasons.
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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 | 0.001 |
| 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 | 0.000 |
| 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".