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Record W2136439611 · doi:10.1093/gji/ggu104

Cross-section electrical resistance tomography of La Soufrière of Guadeloupe lava dome

2014· article· en· W2136439611 on OpenAlexaff
Nolwenn Lesparre, Bartłomiej Grychtol, Dominique Gibert, Jean‐Christophe Komorowski, Andy Adler

Bibliographic record

VenueGeophysical Journal International · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsCarleton University
Fundersnot available
KeywordsGeologyLava domeDome (geology)Electrical resistivity and conductivityElectrical resistivity tomographyElectrical impedance tomographyAndesiteResistive touchscreenVolcanoElectrical conductorLavaElectrical resistance and conductanceTomographySeismologyPetrologyElectrical impedanceGeomorphologyMaterials scienceVolcanic rockElectrical engineeringComposite materialEngineeringOptics

Abstract

fetched live from OpenAlex

The electrical resistivity distribution at the base of La Soufrière of Guadeloupe lava dome is reconstructed by using transmission electrical resistivity data obtained by injecting an electrical current between two electrodes located on opposite sides of the volcano. Several pairs of injection electrodes are used in order to constitute a data set spanning the whole range of azimuths, and the electrical potential is measured along a cable covering an angular sector of ≈120° along the basis of the dome. The data are inverted to perform a slice electrical resistivity tomography (SERT) with specific functions implemented in the EIDORS open source package dedicated to electrical impedance tomography applied to medicine and geophysics. The resulting image shows the presence of highly conductive regions separated by resistive ridges. The conductive regions correspond to unconsolidated material saturated by hydrothermal fluids. Two of them are associated with partial flank collapses and may represent large reservoirs that could have played an important role during past eruptive events. The resistive ridges may represent massive andesite and are expected to constitute hydraulic barriers.

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 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.008
GPT teacher head0.257
Teacher spread0.249 · 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 teacher head, 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

Citations23
Published2014
Admission routes1
Has abstractyes

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