Surface Current Measurement Under Low Sea State Using Dual Polarized X-Band Nautical Radar
Bibliographic record
Abstract
In this paper, a comparison of surface current velocity extraction under low sea state from horizontal and vertical polarized X-band nautical radar image sequences is presented. Three different current retrieval algorithms including the classical least-squares (LS) fitting, a modified iterative least-square fitting routine and an improved normalized scalar product (NSP) method have been employed. An adaptive iteration termination criterion provides optimal times of iteration for the iterative LS method. Variable-search ranges and resolutions are proposed to reduce the computational cost for the NSP method. Field data from two X-band radars deployed during a short experiment at the Skerries Bight near St. John's, Newfoundland is analyzed. Comparison of the results derived from the radar and in-situ buoy data shows that vertical polarization leads to better current measurements than horizontal polarization even under very low sea state. A performance comparison of LS, iterative LS and NSP algorithms indicates that the latter two provide reliable results, with the current measurements from the NSP method being the least variable.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".