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Record W2077805318 · doi:10.1029/2006gl025862

Biases of SRTM in high‐mountain areas: Implications for the monitoring of glacier volume changes

2006· article· en· W2077805318 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeophysical Research Letters · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsShuttle Radar Topography MissionGlacierGeologyPhysical geographyAltitude (triangle)Glacial periodDigital elevation modelRemote sensingGeomorphologyGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.077
GPT teacher head0.318
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it