Validation of SWOT in the Yukon River Basin, Alaska
Bibliographic record
Abstract
Validation of the newly launched Surface Water and Ocean Topography (SWOT) satellite mission is necessary to understand the precision and accuracy of SWOT water surface elevation, slope, and inundation extent measurements in global river, lake, and wetland environments. The upper Yukon and Porcupine river catchments, Alaska, were selected as a validation site due to a high diversity of river morphologies, sizes, and varying adjacent topography at a high latitude, resulting in more repeat SWOT overpasses during the 21-day orbit cycle. Seventy pressure transducers (PTs) were deployed from July through August 2024 in five distinct clusters of reaches, covering a heavily braided section of the Yukon adjacent to low topography, a single-channel section of the Yukon adjacent to high topography, the relatively steep and braided Chandalar River, and two confluences of small (~50m width) rivers with the Porcupine River. Initial results at the node scale suggest that there is ~15 cm difference in water surface elevation at the absolute 68% percentile between SWOT RiverSP and PT measurements. Approximately 30 GNSS long profiles of water surface elevation were collected coincident with SWOT overpasses, facilitating cross-swath evaluation of SWOT data quality and evaluation of reach and node scale products. Initial results at the node scale suggest that there is ~ 19 cm difference in water surface elevation at the absolute 68% percentile between SWOT RiverSP and GNSS measurements. Comparison of in-situ and SWOT water surface elevation and inundation extent data in the Yukon and Porcupine river catchments will help assess the accuracy of SWOT as a new tool for quantifying river slope and discharge. Additionally, two digital elevations models (DEMs) collected over river ice on the Tanana River in April and March, 2025 show ice surface elevation differences of ~ 25 cm at the absolute 68% percentile when compared against SWOT PIXC data aggregated to the node scale.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".