Bibliographic record
Abstract
Radar backscattered signals over the ocean are dampened in extremely high winds, which leads to a wind speed ambiguity problem during the process of wind retrieval from synthetic aperture radar (SAR). This problem was firstly studied by Shen et al. (2007), where we proposed a wind speed ambiguity removal scheme for the two wind speed solutions that may exist for any given normalized radar cross section (NRCS) and wind direction. This approach is based on the operational geophysical model function (GMF) CMOD5. Recently, new C-band GMFs for high wind have been developed, among which, a HH polarized GMF was established for the first time. In this study, the wind speed ambiguity problem will be studied within the context of the available high wind GMFs, which are, CMOD5, CMOD4HW, HWGMF_V and HWGMF_H. For the wind retrieval from HH polarized SAR images, a hybrid empirical polarization ratio is generally adopted. To compare the different behavior of various GMF models, this polarization ratio is used to transform the HH polarized GMF into a VV field. Although the wind speed ambiguity problem is found in most GMFs, the saturation wind speed where radar backscattered signals start to decrease is different for the various GMFs. We show that consideration of the wind speed ambiguity problem is important for high wind retrieval from SAR images, especially for category 5 hurricanes.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".