Wind Speed Estimation From X-Band Marine Radar Images Using Support Vector Regression Method
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Bibliographic record
Abstract
A support vector regression (SVR)-based method for estimating wind speed from X-band marine radar images is proposed. The dependence of histogram pattern of radar images on wind speed and rain condition is first observed. Then, the feature vectors based on bin values of histograms are extracted and trained using an SVR algorithm. Radar images and anemometer data collected from several periods in a sea trial of the east coast of Canada are used for model training and testing. Experimental results show that compared with the ensemble empirical mode decomposition-based methods, the accuracy of wind speed estimation is improved with a reduction of about 0.14 m/s for rain-free images and 0.11 m/s for rain-contaminated images in root mean square error. Moreover, the proposed method also shows high efficiency by greatly reducing the computational time.
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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 | 0.001 | 0.001 |
| 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