Comparison of Jason-2/OSTM, SARAL, and ICESat-1 instrument performance with relevance to research/operational programs focussed on continental waters.
Bibliographic record
Abstract
Standard radar altimetry is applying itself to a number of research and operational-based programs that focus on lakes, reservoirs, river channels and wetland zones. With several decades of surface water level estimates, seasonal and inter-annual variations are being exploited for agriculture and climate analysis. The changes in water heights detected by radar are also used directly as an indicator of hydrologic flux, and are used as input to various algorithms that provide secondary data products including river flow and changes in water storage. Enhanced radar altimetry and laser altimetry (such as SARAL, Cryosat-2, ICESat-1) offers improvements in spatial resolution and accuracy, and multiple data sets allow for cross-validations. Here, we re-examine the performance of the Jason-3 and GLAS altimeters, and compare it to the performance of the AltiKa altimeter with relevance to on-going research and operational programs. Focus is on target acquisition times and elevation accuracy, and the merits of combining data sets. Regions of study include the Yukon River, the Usangu wetlands, and various small lakes, reservoirs and irrigation enclosures around the globe. Applications are both national and international with basin hydrology, conservation of ecosystems, and water resources interests and objectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 teacher head, 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".