Monitoring Diesel Spills in Freezing Seawater under Windy Conditions Using C-Band Polarimetric Radar
Bibliographic record
Abstract
The risk of oil spills in the Arctic is growing rapidly as anthropogenic activities increase due to climate-driven sea ice loss. Detecting and monitoring fuel spills in the marine environment is imperative for enacting an efficient response to mitigate the risk. Microwave radar systems can be used to address this issue; therefore, we examined the potential of C-band polarimetric radar for detecting diesel fuel in freezing seawater under windy environmental conditions. We present results from a mesocosm experiment, where we introduced diesel fuel to a seawater-filled cylindrical tub at the Sea-ice Environmental Research Facility (SERF), University of Manitoba. We characterized the temporal evolution of the diesel-contaminated seawater and sea ice by monitoring the normalized radar cross section (NRCS) and polarimetric parameters (i.e., copolarization ratio (Rco), cross-polarization ratio (Rxo), entropy (H), mean-alpha (α), conformity coefficient (μ), and copolarization correlation coefficient (ρco)) at 20° and 25° incidence angles. Three stages were identified, with notably different NRCS and polarimetric results, related to the thermophysical conditions. The transition from calm conditions to windy conditions was detected by the 25° incidence angle, whereas the transition from open water to sea ice was more apparent at 20°. The polarimetric analysis demonstrated that the conformity coefficient can have distinctive sensitivities to the presence of wind and sea ice at different incidence angles. The H versus α scatterplot showed that the range of distribution is dependent upon wind speed, incidence angle, and oil product. The findings of this study can be used to further improve the capability of existing and future C-band dual-polarization radar satellites or drone systems to detect and monitor potential diesel spills in the Arctic, particularly during the freeze-up season.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".