A Benchmark Dataset of Water Levels and Waves for SWOT Calibration and Validation: Insights from the St. Lawrence Estuary and Saguenay Fjord
Bibliographic record
Abstract
The St. Lawrence Estuary and Saguenay Fjord (Quebec, Canada) form a macro-tidal, seasonally ice-covered system exhibiting strong spatial and temporal gradients in water surface dynamics due to the interaction of tides, river discharge, internal waves, and complex bathymetry. During the SWOT mission’s Calibration/Validation (Cal/Val) phase (April–July 2023), this region was advantageously located under the fast-sampling orbit, acquiring near-daily, high-resolution KaRIn observations along a ~300 km reach of the estuary. To support SWOT validation and enable new scientific investigations, we assembled a comprehensive multi-sensor dataset combining in-situ and remote sensing measurements. The dataset includes tide gauges and wave buoys, GNSS-IR sensors, HF radars, ADCPs, airborne LiDAR and AirSWOT measurements, and satellite altimetry (RCM, Sentinel), spanning both the pre- and post-launch Cal/Val and Science phases of SWOT. These observations were co-located in time and space with SWOT overpasses to provide reference measurements of water surface elevation, slope, and surface wave variability under various environmental conditions, including ice and storm events. Building on this dataset, we apply an advanced harmonic analysis approach tailored to short and/or sparsely sampled SWOT records, blending tide gauge information to achieve super-resolution of non-stationary tides. Particular attention is given to the fluvial–estuarine transition zone (FETZ), where conventional observations are spatially sparse and physical processes are nonlinear and nonstationary. This curated dataset represents one of the most complete field observation efforts ever conducted in a cold-region estuary for satellite altimetry, offering a valuable resource for validating SWOT performance and improving our understanding of estuarine hydrodynamics. It is designed to support cross-comparisons with models and to advance coastal altimetry applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".