Biases of SRTM in high‐mountain areas: Implications for the monitoring of glacier volume changes
Bibliographic record
Abstract
Because of its nearly global coverage, the Shuttle Radar Topographic Mission (SRTM) topography is a promising data set for estimating mountain glacier volume changes. But, first, its absolute accuracy must be thoroughly investigated in a glacial environment. We use topographic data available in the French Alps to assess the usefulness of SRTM for the monitoring of glacier volume variations. We observe clear biases with altitude both on ice‐free and glacier‐covered areas. At high altitudes, SRTM elevations are underestimated by up to 10 m. These biases can have a significant impact on any estimate of glacier volume changes. If SRTM is the most recent of the two compared topographies, the volume loss is overestimated (and vice versa). We cannot conclude definitively on the origin of these biases and whether they affect all high‐mountain areas but our findings invite reconsideration of previous estimates of glacier wastage based on SRTM.
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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 | 0.005 | 0.015 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".