Wind parameter measurement using X-band marine radar images
Bibliographic record
Abstract
Chapter Contents: 17.1 Wind streaks/wind gusts based methods 17.1.1 Local gradient based method 17.1.2 Optical flow based method for wind vector retrieval 17.2 Intensity information and curve fitting based methods 17.2.1 Single curve fitting based algorithm 17.2.2 Two-model curve fitting for rain mitigation 17.2.3 Dual curve fitting for low sea state cases 17.2.4 Significant wave height incorporated curve fitting 17.2.5 Intensity level selection algorithms 17.2.6 Modified ILS 17.2.7 Texture analysis incorporated ILS 17.3 Transform domain and curve fitting based methods 17.3.1 Spectral noise based algorithm 17.3.2 Spectral integration based algorithm 17.3.3 Ensemble empirical mode decomposition based methods 17.4 Nonparametric regression based methods 17.4.1 Neural network based method 17.4.2 Support vector regression based method 17.4.3 Gaussian process regression based method 17.5 Error mitigation 17.6 Conclusions and outlook References
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".