Satellite synthetic aperture radar detection of Delaware Bay plumes: Jet‐like feature analysis
Bibliographic record
Abstract
From the physics of radar imaging of ocean surface processes a theoretical model for the radar imaging of an ocean jet was derived. The theoretical model predicts that the jet current structure, sech 2 η, constitutes a determining factor for the radar image. The modeled image intensity depends on the axial velocity, decreasing as x −1 along the jet axis, and is sensitive to the wind direction with respect to the jet axis. The model was used to interpret synthetic aperture radar (SAR) images of Delaware Bay plumes taken by the RADARSAT‐1 (Canadian Radar Satellite) and ERS‐2 (European Remote Sensing satellite) satellites during a period from summer 1996 to spring 1997. In all seasons the low‐salinity plumes appear as relatively bright, jet‐like patterns on SAR images. From a morphological interpretation of the image features, both summer and winter plumes can be divided into three sections along the axis: source, jet, and dispersion region. Along the transverse direction, SAR image interpretations indicate that the plumes have a twin jet structure, which conforms to decomposition of field measurements. In summer the typical axial velocity is estimated at 6 × 10 −1 m s −1 with a Reynolds number of 18. At about 5 km downstream from the source the plume behaves like a turbulent jet, and beyond that range down to about 10 km downstream, it behaves like a laminar jet. In winter the plumes become weaker than in summer. The typical axial velocity is estimated at 4 × 10 −1 m s −1 with a Reynolds number of 3. The jet behaves like the laminar jet off the source down to about 4 km downstream. In both summer and winter cases the SAR images were taken at maximum flood tide, and the plumes appeared as jets. In the spring case the SAR image was taken at early flood tide; the plume appeared as an integrated body with relatively uniform bright tunes. In all cases the plume disperses within about 25 km downstream.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".