Characterization of oil slicks at sea using remote sensing techniques
Bibliographic record
Abstract
Oil slicks can be visually detected through remote sensing techniques because of sharp image contrast variations between the oil slicks and surrounding water. These contrast variations are usually due to the dampening of the water surface roughness caused not just by oil, but possibly also by freshwater runoff and biogenic surfactants (also called “biogenic look-alikes”), such as those due to phytoplankton blooms. Floating macroalgae can also alter the texture of the water surface and contribute to look-alikes. Using methodologies we developed and implemented in previous studies of oil spills using hyperspectral optical imagery, we have tested several algorithms for biogenic look-alikes and oil slick characterization from optical and RADAR sensors in order to improve operational monitoring of marine coastal areas for oil pollution. With the opportunity to use imagery acquired over the Deepwater Horizon oil spill in the Gulf of Mexico in 2010, we have demonstrated promising utility of optical imagery to assist in differentiating oil from RADAR look-alikes in low wind situations. We have demonstrated that the interpretation of false positives for oil slicks in RADAR imagery can be adequately assisted by the analysis of optical imagery. Furthermore, oil spill extent and slick thickness can be mapped and characterized using spaceborne imagery. This represents a major improvement over local observations of oil spill for emergency and mitigation actions by improving response time and providing a synoptic view of the impacted areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".