MétaCan
Menu
Back to cohort
Record W2025492279 · doi:10.1093/gji/ggt282

Monitoring the excavation damaged zone by three-dimensional reconstruction of electrical resistivity

2013· article· en· W2025492279 on OpenAlexaff
Nolwenn Lesparre, Dominique Gibert, Florence Nicollin, Christophe Nussbaum, Andy Adler

Bibliographic record

VenueGeophysical Journal International · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsCarleton University
Fundersnot available
KeywordsExcavationGeologyElectrical resistivity and conductivityElectrical resistivity tomographyBeddingMining engineeringGeotechnical engineeringBedAnisotropyEngineering

Abstract

fetched live from OpenAlex

A damaged zone is formed during the excavation of underground galleries, altering the rock properties. From a perspective of nuclear waste storage in deep geological sites, there is a clear interest to monitor the rock properties in such zones. We constructed electrical resistivity tomograms as a function of time to monitor the damaged area in gallery 04 of the Mont Terri underground rock laboratory (Switzerland). Measurements were performed using electrode rings surrounding the gallery. The experience showed a heterogeneous distribution of damages around the gallery and their fast formation after the excavation. Two main areas were concerned by damage formation, located in regions where the bedding was tangential to the excavated gallery. Such regions represented an extension of about 2 m along the gallery walls and reached a depth of 1.5 m. Main damages were created during the next months following the excavation process. Slight variations were still observed 3 yr after the excavation that may be related to the gallery environmental condition fluctuation. The method applied here demonstrates the interest to monitor the whole region surrounding excavated galleries dedicated to host nuclear wastes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.238
Teacher spread0.224 · 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.

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

Citations9
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueGeophysical Journal InternationalSame topicGeophysical and Geoelectrical MethodsFrench-language works237,207