Multi-altimeter observations of the Yukon and Copper Rivers in Alaska: Assessment of the determination of river discharge within these complex river systems
Bibliographic record
Abstract
Both radar and laser altimetry can be utilized to monitor both water level variations and channel surface gradients for the largest river systems around the world. Here, we focus on the Yukon and Copper Rivers in Alaska. Despite their extent and complexity, few US and Canadian gauges exist across the basins. This hampers modelling and basin dynamics efforts, particularly affecting flood predictions and fisheries analysis. Both conventional (Jason-2, ENVISAT, SARAL, Jason-3) and Delay-Doppler (CRYOSAT-2, Sentinel-3A) radar altimetry, and laser altimetry (ICESat-1), offers spatially and temporally varying measurements, and multiple data sets allow for cross-validations. Innovative fully-focused SAR data processing techniques also offer improved along-track spatial resolution. Here, we examine the performance of the various instruments and techniques with a focus on river reach acquisition and improved elevation accuracy. We also discuss the merits of combining the data sets and look to their application with respect to i) the determination of river discharge and ii) isolating contributions to discharge from glacial and tundra melt waters.
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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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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".