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Record W2158290110 · doi:10.1109/igarss.2009.5418050

Determination of variations in glacier surface movements through high resolution interferometry; Bylot Island, Canada

2009· article· en· W2158290110 on OpenAlexaffabout
Ken Whitehead, Brian J. Moorman, Pablo Wainstein

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGlacierGeologyGeodesyDisplacement (psychology)WeightingInterferometryFlow (mathematics)ArcticGeomorphologyRemote sensingGeometryOpticsMathematicsPhysicsOceanography

Abstract

fetched live from OpenAlex

Interferograms were generated from 10 TerraSAR-X image pairs, with the objective of obtaining estimates of winter surface motion for a slow-moving polythermal arctic glacier. Flow directions were computed using both ascending and descending-pass interferograms for each period, with the median value being adopted as the final direction. The weighted average flow was computed, with weighting based on the inverse of the difference between the ascending and descending-pass displacement estimates for each date. This study uses multiple interferograms with different imaging geometries to provide estimates of down-glacier flow. The methodology adopted minimizes the effects of glacier / satellite track alignment and those resulting from vertical motion of the glacier surface. Current velocities were compared with flow estimates derived from a 1992 ERS-1 image pair. The velocities were similar over most of the glacier, but current velocities were found to be 30% to 50% lower on the lower glacier.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.218
Teacher spread0.202 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2009
Admission routes2
Has abstractyes

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