A new nonlinear approach to extraction of ocean wave spectra from bistatic Doppler HF-radar data
Bibliographic record
Abstract
Knowledge of the ocean wave spectrum, from which many important ocean parameters can be extracted, is crucial to understand the ocean's behaviour. Due to the intricate nature of ocean wave spectrum extraction from HF radar data, previously-devised methods relied on making a linear approximation to the original nonlinear problem or resorted to constraining the solution space, in order to simplify the solution process. This problem is aggravated in a bistatic configuration due to its geometrical complexity. The present work proposes a change of variables in the second-order radar cross-section from bistatic HF-Radar in order to extract the ocean power spectral density from Doppler HF-Radar data. The main advantage of the proposed method is the possibility of extracting the ocean wave spectrum without assuming any linear approximation to the inverse problem. In this work, it is found that the proposed method can accurately extract the ocean wave spectrum from bistatic HF radar data under a variety of ocean conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".