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Record W2537933417 · doi:10.1109/userest.2008.4740337

Surface deformation studies of Tenerife Island, Spain from joint GPS-DInSAR observations

2008· article· en· W2537933417 on OpenAlexafffund
Sergey Samsonov, K. F. Tiampo, Pablo J. González, Juan F. Prieto, Antonio G. Camacho, José Fernández

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaTechnische Universiteit Delft
KeywordsGeodesyGlobal Positioning SystemGeologyGeodetic datumRemote sensingSIGNAL (programming language)SeismologyComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

This work presents results for the three-dimensional displacement field at Tenerife Island calculated from campaign GPS and ascending and descending ENVISAT DInSAR interferograms. The goal of this work is to provide an example of the flexibility of the technique by fusing together new varieties of geodetic data, and to observe surface deformations and study precursors of potential activity in volcanic regions. Interferometric processing of ENVISAT data was performed with GAMMA software. All possible combinations were used to create interferograms and then stacking was used to increase signal-to-noise ratio. Decorrelated areas were widely observed, particularly for interferograms with large perpendicular baseline and large time span. Tropospheric signal was also observed which significantly complicated the interpretation. Subsidence signal was observed in the NW part of the island and around Mount Teide and agreed in some regions with campaign GPS data. It is expected that the technique will provide better results when more high quality DInSAR and GPS data is available.

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 score0.060
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.243
Teacher spread0.183 · 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

Citations6
Published2008
Admission routes2
Has abstractyes

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