Ocean wave spectrum estimation from marine radar data using the polar fourier transform
Bibliographic record
Abstract
The Polar Fourier Transform (PFT) can be used to estimate the ocean wave number spectrum from marine radar data. The main advantage of the PFT over the Cartesian Fourier Transform (CFT) is its direct applicability to the native radar data without the need for the intermediate stage of scan conversion. However, by definition, the PFT operates on positive, and not negative, wavenumbers. This leads to a problem in estimating the ocean wave spectrum which may contain negative wavenumbers. Therefore, a process of wavenumber mapping is proposed in order to circumvent this problem. In this technique, which is implemented before applying the PFT, the wave number content of the radar signal is compressed by a factor of 1/2 and shifted by π/2. This mapping migrates all wave numbers to fit into the range of 0 to π so that the PFT is required to deal with only positive wavenumbers.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".