Bibliographic record
Abstract
It is possible to estimate the radial velocity of a target by comparing the Doppler centroid, as estimated from the return from the target, with a "reference" Doppler centroid. Of particular interest is the estimation of one component of an ocean current, where the target is an area of ocean a few square kilometres in extent. Studies using single beam SAR products have shown this technique to be effective in estimating the location of the Gulf Stream and the Loop Current in the Gulf of Mexico, but the size of these features means that a swath width of greater than 100 km would be desirable. This immediately suggests working with ScanSAR data, where a wider swath is formed by combining several beams. However, adapting the technique to such data presents a number of difficulties. The spectral information needed for Doppler estimation is not available from standard products, thus custom processing of raw data is necessary. For each beam we have burst mode rather than continuous data, which complicates the spectral shape of the target return but this problem can be avoided by the appropriate choice of azimuth patch size. Combining estimates from several beams involves problems with independent errors and beam edge corruption. The "reference" Doppler centroid must be estimated over a large range and azimuth extent. This estimation is accomplished by fitting a quasi-physical model, involving orbital information, satellite attitude and information about the satellite antenna. The fitting problem is examined, with specific reference to RADARSAT and ENVISAT. Examples of ocean current estimation using RADARSAT ScanSAR data are shown.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".