Validation of Surface Elevation from TanDEM-X Digital Elevation Models of Devon Island Ice Cap, Canadian High Arctic
Bibliographic record
Abstract
Digital elevation models (DEMs) generated by the TanDEM-X mission from synthetic aperture radar interferometry (InSAR) provide an excellent potential resource to study the geodetic mass balance of glaciers. However, they typically suffer from a negative bias in surface elevations on glacierized terrain due to penetration of radar waves into the snowpack and firn. In this thesis, TanDEM-X DEMs from 2010 to 2018 over Devon Island Ice Cap (DIC), Canadian High Arctic, are validated using independent surface elevation datasets from ArcticDEM, IceBridge laser altimetry and ground GPS transects. In agreement with previous studies using TanDEM-X DEMs, the bias is most significant in the percolation and saturation zones (i.e., accumulation area), where volume scattering and loss of interferometric coherence is greatest compared to the bare ice zone. The average elevation bias on DIC is -2.74 ±0.25 m, which should be accounted for in error budgets for geodetic mass balance estimates to be reliable.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".