Sea state measurements from TS-X SAR data \n \n
Bibliographic record
Abstract
An empirical algorithm XWAVE to derive significant wave height from high resolution TerraSAR-X (TS-X) and \nTandem-X (TD-X) has been developed. The algorithm is created especially for spaceborne X-band SAR data without \nneeding a priori information. TS-X scenes were acquired in the over buoys located at the coast of United States of \nAmerica and Canada and including Hawaii islands. Integral wave parameters are estimated from TS-X and TD-X data \nby integration two-dimensional image spectra and using geophysical function, for which the parameters are calibrated. \nTS-X Stripmap images and in-situ buoy measurements data is used to tune geophysical model parameters. The tuning is \ncarried out by comparisons of integral wave parameters as wave height, peak period and direction (1) and by the shape \nof 1-D in-situ measured spectra and 1-D integrated image spectra (2). The results show that the model can yield sea \nstate measurements up to 5m significant wave height by TS-X SAR with an scatter index of 0.21 . The model can be \nused for the near real-time services to deliver sea state measurements
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".