Iterative least‐squares‐based wave measurement using X‐band nautical radar
Bibliographic record
Abstract
In this study, an existing iterative least‐squares (LS) method for determining ocean surface currents from X‐band nautical radar images is modified for extracting ocean wave information from the same data. Within each iterative step, the image spectra samples are classified as containing contributions from fundamental, first‐order and higher harmonic waves or noise. Based on the classification result, a new scheme is proposed to increase the robustness of current estimation. This involves automatically adjusting the first threshold that is used for obtaining an initial guess of the current velocity in the iterative LS method. The proposed wave algorithm directly uses the classified fundamental and first‐order harmonic wave components for wave spectra and parameter retrieval. Unlike previous wave analysis techniques in which a bandpass filter is required to eliminate the non‐wave contributions after the current velocity is obtained, the proposed algorithm simplifies the wave retrieval without such a bandpass filter. Algorithm verification is first conducted by using simulated radar images. Subsequently, the method is applied to field radar data and the results are compared with wave‐buoy measurements. Wave parameters from both types of data show that the modified wave algorithm produces results that are close to those achieved using traditional algorithms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".