Peace-Athabasca Delta water surface elevations and slopes mapped from AirSWOT Ka-band InSAR
Bibliographic record
Abstract
In late 2023 the Surface Water and Ocean Topography (SWOT) satellite mission will release unprecedented high-resolution measurements of water surface elevation (WSE) and water surface slope (WSS) globally. SWOT’s exciting Ka-band near-nadir wide-swath interferometric radar (InSAR) technology could transform studies of surface water hydrology, but remains highly experimental. We examine Airborne SWOT (AirSWOT) data acquired twice over Canada’s Peace-Athabasca Delta (PAD), a large, low-gradient, ecologically important riverine wetland complex. While noisy and susceptible to “dark water” (low-return) data losses, spatially averaged AirSWOT WSE observations reveal a broad-scale water-level decline of ~44 cmn (σ =271 cm) between 9 July and 13 August 2017, similar to a ~56 cm decline (σ=33 cm) recorded by four in situ gauging stations. River flow directions and WSS are correctly inferred following filtering and reach-averaging of AirSWOT data, but ~10 km reaches are essential to retrieve them. July AirSWOT observations suggest steeper WSS down an alternate flow course (Embarras River–Mamawi Creek distributary) of the Athabasca River, consistent with field surveys conducted the following year. This signifies potential for the Athabasca River to avulse northward into Mamawi Lake, with transformative impacts on flooding, sedimentation, ecology, and human activities in the PAD. Although AirSWOT differs from SWOT, we conclude SWOT Ka-band InSAR observations may detect water level changes and avulsion potentials in other low-gradient deltas globally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".