The “Surface Water Ocean Topography” satellite, a major step forward in understanding the oceans
Bibliographic record
Abstract
The French-U.S. SWOT (Surface Water Ocean Topography) satellite with contributions from Canada and the UK, launched in December 2022, has been delivering surface water height data of exceptional quality from all over the globe for more than 18 months now.Led by the French space agency CNES (Centre National d’Études Spatiales) and NASA (National Aeronautics and Space Administration), the SWOT mission is able to measure and survey water on over 90% of Earth’s surface, providing a high-resolution map of our planet’s water resources for the first time ever. The satellite’s measurements of surface water and ocean heights will help to further in-depth studies of water resource management and revolutionize our understanding of the global water cycle and how it is being affected by climate change.The satellite’s wide-swath interferometric radar sensor provides a detailed picture of sea surface height at a resolution of two kilometres and of surface water bodies wider than 100 metres with a revisit frequency of 21 days. SWOT data draw on a heritage of 30 years of continuous progress in the field of satellite altimetry and are the culmination of several decades of French-U.S. space cooperation. SWOT is able to detect eddies ten times smaller than anything seen by previous satellite altimetry missions.This new vision of the oceans and coastal regions should help us to delve deeper into the role of small eddies in shaping climate, as well as their relationship with major ocean currents and zones rich in biodiversity. It brings a new dimension to ocean research, enabling closer interactions between physical oceanography and biological productivity and paving the way for better management of the marine environment (e.g. through the identification and creation of Marine Protected Areas).
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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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".