An alternative method for surface current extraction from X-band marine radar images
Bibliographic record
Abstract
In this paper, a novel current inversion algorithm from X-band marine radar images is proposed. The routine begins with a 3D-FFT of the radar image sequence, followed by the extraction of the dispersion shell from the 3-D image spectrum. After a polar coordinate transformation, the "polar current shell" is then analyzed to retrieve current information such as the speed and direction of encounter. Particularly, a Grubbs' test is conducted to remove outliers along each radial direction, and a robust sinusoidal curve fitting is applied to the data points along each circumferential direction. For validation, the algorithm is tested against simulated radar PPI images. The results indicate that the proposed procedure, unlike most existing current inversion schemes, is not susceptible to high current speeds and has no direction restriction. Meanwhile, the relatively low computational cost makes it an excellent choice in practical marine applications.
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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) | 0.001 | 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".