Generalised noise cancellation method for wave estimation by HF surface wave radar
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
High frequency (HF) surface wave radar (HFSWR) has been demonstrated, in many experiments and papers, to be a powerful tool for sea‐state detection. However, the availability and accuracy of the HFSWR measurements are limited by various unwanted clutter and interferences (collectively called ‘noise’) that contaminate the radar received signals, especially for wave estimation. This study extends the image recognition, segmentation and subspace projection method for removing the radio frequency interference developed in the previous study, to the mitigation of more general types of noise. Applications of this generalised method are presented. The results show that the noise can be largely removed regardless of their correlation in Doppler or range, their size in the range‐Doppler domain and whether they are homogeneous or inhomogeneous. The effectiveness of all these approaches is validated by using data obtained with the Pisces HF radar, which is a high‐performance radar developed for long‐range wave measurement, operating in the lower half of the HF band (5–10‐MHz).
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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 | 0.001 | 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) | 0.000 | 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 it