Engaging the Applications Community of the future Surface Water and Ocean Topography (SWOT) Mission
Bibliographic record
Abstract
Abstract. NASA and the French space agency, CNES, with contributions from the Canadian Space Agency (CSA) and United Kingdom Space Agency (UKSA) are developing new wide swath altimetry technology that will cover most of the world’s ocean and surface freshwater bodies. The proposed Surface Water and Ocean Topography (SWOT) mission will have the capability to make observations of surface water (lakes, rivers, wetland) heights and measurements of ocean surface topography with unprecedented spatial coverage, temporal sampling, and spatial resolution compared to existing technologies. These data will be useful for monitoring the hydrologic cycle, flooding, and characterizing human impacts on a changing environment. The applied science community is a key element in the success of the SWOT mission, demonstrating the high value of the science and data products in addressing societal issues and needs. The SWOT applications framework includes a working group made up of applications specialists, SWOT science team members, academics and SWOT Project members to promote applications research and engage a broad community of potential SWOT data users. A defined plan and a guide describing a program to engage early adopters in using proxies for SWOT data, including sophisticated ocean and hydrology simulators, an airborne analogue for SWOT (AirSWOT), and existing satellite datasets, are cornerstones for the program. A user survey is in development and the first user workshop was held in 2015, with annual workshops planned. The anticipated science and engineering advances that SWOT will provide can be transformed into valuable services to decision makers and civic organizations focused on addressing global disaster risk reduction initiatives and potential science-based mitigation activities for water resources challenges of the future. With the surface water measurements anticipated from SWOT, a broad range of applications can inform inland and coastal managers and marine operators of terrestrial and oceanic phenomena relevant to their work.
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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 | 0.029 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".