Retrievals of sea surface temperature fronts from SAR imagery
Bibliographic record
Abstract
We present a new methodology to identify SST fronts of the Gulf Stream, using linear relationships between sea surface temperature (SST) gradients, and curl and divergence of wind stress fields, derived from high resolution (1 km) SAR data. The new approach uses a composite metric determined from the wind stress divergence and curl fields from individual SAR images. Multi‐stage spatial filtering, Wiener and Gaussian low‐pass filters, and a statistically‐based high pass spatial filter are applied to the derived wind stress curl and divergence fields. Results are significantly improved by restricting SAR imagery to cases where wind speed is less than 12 m/s, thus removing strong wind shear fronts. The method is demonstrated with SAR images of the Gulf Stream and has potential to be applied in near real time operations. The advantages of SAR imagery over optical sensors are its independence of cloud or night‐time conditions and high accuracy.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".