Sea surface oil slick detection from GNSS-R Delay-Doppler Maps using the spatial integration approach
Bibliographic record
Abstract
This paper investigates the methodology of applying the spatial integration approach (SIA) to the scattering coefficient retrieval from GNSS-R signals for sea surface oil slick detection. The SIA has been traditionally used in the generation of simulated Delay-Doppler Maps, which emphasizes the power contribution of every sea surface point. While in this paper, the SIA is applied to the scattering coefficient retrieval with a reasonable approximation to relate the Delay-Doppler domain and the spatial domain. To validate this approach, a simulated Delay-Doppler Map over an oil slicked area based on the measured oil data and wind speed of an actual oil spill event is analyzed. Particularly, the configuration of the UK-DMC GNSS-R receiver, which includes its sampling rate and noise level, is considered in the oil slick detection process. A theoretical comparison between the commonly used Jacobian approach and the SIA reveals their differences in methodology and the cause of errors. The result indicates that with acceptable extra time consumption, the SIA increases the accuracy of the scattering coefficient retrieval algorithm, especially in the area surrounding the ambiguity-free line.
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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 | 0.000 |
| 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) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".