Real-time open ocean wind waves from navigation radars for a truly global wind wave operational observing system
Bibliographic record
Abstract
Global information about ocean wind waves is crucial for understanding their role in the climate system, validating model outputs, and assessing risks for shipping and marine structures. Recent advances in marine radar technologies have enabled accurate, high-resolution measurements of surface wind waves and their spectral characteristics. Making these measurements available in real-time opens a wide new range of products for many user communities. Here we introduce SeaVision, a ship-based monitoring system that, once integrated into a standard shipborne X-band radar, considerably improves real-time observational networks along major shipping routes. SeaVision automatically measures significant wave height, peak period and directional wave spectra at temporal resolutions down to seconds. First developed for research purposes in 2020, SeaVision passed an extensive period of validation using Spotter wave buoys and satellite data. Validation onboard research vessels was conducted for a wide range of latitudes, from the Arctic to Antarctica. SeaVision is fully operational, cost-effective, and capable of transmitting wave parameters continuously via satellite. Further developments of SeaVision allow for retrieving near surface wind speed, surface currents and ice parameters with the same resolution. Extensive installations of SeaVision (as well as similar systems) onboard commercial and research vessels allow for establishing a near-global observational network (as a part of GCOS and GOOS) largely exceeding capabilities of the present VOS network which over the last few decades are experiencing a dramatic decline and is also regionally complementing satellite missions. SeaVision will enhance coverage of the so far inadequately sampled global oceans.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".