SWOT Level-3 Overview algorithms and examples
Bibliographic record
Abstract
The Surface Water Ocean Topography (SWOT) mission was launched in December 2023.It is the result of cooperation between CNES, NASA and their partners from the Canadian and UK Space Agencies. SWOT carries a unique altimetric payload, including a Ku-band Jason-class nadir altimeter and a Ka-band SAR-interferometric (KaRIn) wide-swath altimeter providing 2 swaths 50-km wide. It offers new opportunity for the observation of the small mesoscale structures over the oceans, including near coast and high latitude areas. Thanks to these observation capabilities, SWOT could contribute to a better understanding of the physical processes at play at these scales, and to the applications that flow from them.Few months after its launch, Level-2 product of the KaRIn measurement were made available for the SWOT Science Team. These products however remain complex and oriented for the altimetry expert community, while many non-expert users may need the swath measurement for different applications. To answer these needs, a Level-3 product was developed in the context of the SWOT Science Team Project DESMOS. It is the result of different processing steps including the use of the state of the art of different geophysical corrections (e.g. Mean Sea Surface, ocean tide), aiming to improve the quality of the sea level measurement at small mesoscale; the multi-mission calibration, that makes the SWOT measurements consistent with other altimeters; the data selection, to identify invalid measurements; the sea surface height noise-mitigation, aiming reduce the noise level on SSHA and allowing the estimation of the geostrophic current and vorticity. We present here the SWOT KaRIn Level-3 product.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.045 |
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".