Bibliographic record
Abstract
ABSTRACT High-frequency (HF) radars remotely measure ocean near-surface currents based on the Doppler shift of electromagnetic waves backscattered by surface gravity waves with half the electromagnetic wavelength, called Bragg waves. Since their phase velocity is affected not only by wave–current interactions with vertically sheared mean Eulerian currents but also by wave–wave interactions with all the other waves present at the sea surface, HF radars should measure a quantity related to the Stokes drift in addition to mean Eulerian currents. However, the literature is inconsistent—both theoretically and experimentally—on the specific expression and even on the existence of the Stokes drift contribution to the HF radar measurements. Three different expressions that have been proposed in the literature are reviewed and discussed in light of the relevant published experimental results: 1) the weighted depth-averaged Stokes drift, 2) the filtered surface Stokes drift, and 3) half of the surface Stokes drift. Effective measurement depths for these three expressions are derived for the Phillips wave spectrum. Recent experimental results tend to discard the second expression but are not inconsistent with the first and third expressions. The latter is physically appealing, since it is a quasi-Eulerian quantity that would be measured by a current meter at a fixed horizontal position but allowed to follow the free surface moving vertically up and down with the passage of the waves. A definitive answer will require further experimental investigations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".