The potential of TanDEM-X repeat acquisitions to monitor elevation and mass changes of Arctic and Antarctic glaciers
Bibliographic record
Abstract
The large ice caps and glaciers of the northern and southern polar regions have the potential to contribute significantly to global sea-level rise, yet measurements of glacier mass changes in those regions are scarce and difficult due to harsh conditions and the size of Arctic and Antarctic glacier areas. Acquisitions of the synthetic aperture radar satellite mission TanDEM-X provide valuable insights into glacier dynamics in those regions as the X-band radar is independent from clouds and illumination and can resolve elevation changes of large glacierized areas as well as individual glaciers. We use specifically generated and coregistered digital elevation models (DEM) from repeated TanDEM-X data takes to derive glacier elevation changes between 2010 and 2020. For the Arctic regions, we already calculated elevation changes for the Russian Arctic archipelagos from TanDEM-X acquisitions (2000-2017). Currently, we are preparing similar TanDEM-X DEM differences for Arctic glaciers outside the Greenland ice sheet (Svalbard, Iceland, Alaska, Canadian Arctic, Scandinavia and North Asia). In contrast to the wide and smooth areas of the East and West Antarctic ice sheets, the steep topography of the Antarctic Peninsula strongly limits the application of altimeter data for accurately quantifying glacier mass changes. Therefore, we computed glacier mass changes along the Antarctic Peninsula by means of TanDEM-X data. Additionally, measurements of the IceSAT2 laser altimeter will be integrated in the analysis to improve the estimation of radar signal penetration into snow and firn and thereby reduce the elevation change and mass balance uncertainties.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".