High resolution sea state parameters estimated from SAR imagery at Herschel Island, Qikiqtaruk, Yukon, Canada
Bibliographic record
Abstract
Sea state parameters such as significant wave height were estimated using the empirical CWAVE_EX algorithm. The aim of the data acquisition was to overcome the lack of in-situ data on significant wave heights in the Arctic by using remote sensing data. Synthetic Aperture Radar (SAR) images from the TerraSAR-X (TS-X) and TanDEM-X (TD-X) satellites were used to obtain high spatial resolution sea state information around Herschel Island, Qikiqtaruk, Yukon, Canada. All ice-free scenes were processed from the entire archive of TS-X/TD-X StripMap mode imagery with a coverage of approximately 30 km x 50 km acquired between 2009 and 2020. For each SAR scene, a sea state file was created as a tab-separated text file in the coordinate reference system EPSG: 4328 - WGS84. The dataset was used to analyse wave heights in the nearshore zone according to spatial variability, seasonality and wind conditions. For more details please refer to Brembach, K., Pleskachevsky, A., Lantuit, H. (in prep): Investigating High-Resolution Spatial Wave Patterns on the Canadian Beaufort Shelf using SAR Imagery at Herschel Island, Qikiqtaruk, Yukon, Canada.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".