Dealing with challenges of altimetry-based surface water height derivation over boreal catchments : case study of Mackenzie river
Bibliographic record
Abstract
The Earth is a watery place, which fills the oceans, rivers, and lakes. Approximately 71% of the Earth’s surface is water-covered. Measurements of surface water level in oceans, lakes, rivers and coastal waters are important for a variety of reasons. In the short term, this can, for example, help to alert to dangerous water level so that actions can be taken in advance, while in the long term, monitoring water levels can provide even greater insight into patterns of water dispersal in the area, and measure, for example, the effects of global warming. Satellite altimetry, which was originally designed for oceanography in the 1970s, has revolutionized our knowledge of the marine gravity field, of the dynamics of the oceans and even ofland hydrology. It is a space measurement technique that uses artificial satellites to measure the altitude from the satellite to the Earth’s surface. Due to its high resolution, global coverage and short revisit time, it is playing an increasingly important role in measurements of water level. For some years, this technology has also been used to retrieve water levels from rivers, lakes, and any inland water body as well. However, compared with the wide seas, measurements of inland water bodies involve many challenges. In this paper, we will take the Mackenzie River in northern Canada as a research object and process the data through the Matlab-based program Atlbundle+ to study the potential of inland altimetry, the problems it faces and the possible ways to find the solution. In general, there are two perspectives that will be considered: Firstly, how we can accurately remove outliers in the measurements, and secondly, how to improve the altimetry-driven water level time series by improving the retracking methods. Finally, based on the results and the procedure, a systematic analysis of the inland altimetry can be carried out.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".