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Record W2131039377 · doi:10.1144/sp400.33

Enhanced Sealing Project (ESP): evolution of a full-sized bentonite and concrete shaft seal

2014· article· en· W2131039377 on OpenAlexaffabout
David A. Dixon, D. G. Priyanto, J.B. Martino, M. de Combarieu, Rune Johansson, P. Korkeakoski, J. E. Villagran

Bibliographic record

VenueGeological Society London Special Publications · 2014
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsNuclear Waste Management OrganizationAtomic Energy (Canada)
Fundersnot available
KeywordsSeal (emblem)BentoniteGeologyGeotechnical engineeringMining engineeringArchaeology

Abstract

fetched live from OpenAlex

Abstract A full-scale shaft seal was designed and installed in the 5 m-diameter access shaft at Atomic Energy of Canada Limited's (AECL's) Underground Research Laboratory at the point where the shaft intersects an ancient water-bearing, low-angle thrust fault at a depth of c. 275 m in granitic rock. The seal consists of a 6 m-thick bentonite-based component sandwiched between 3 m-thick, keyed upper and lower concrete components. This design was adopted in order to limit the mixing of saline groundwater from the deeper regime with the fresher, near-surface groundwater regime. Construction of the shaft seal was done as part of Canada's Nuclear Legacy Liabilities Program. A jointly funded monitoring project, called the Enhanced Sealing Project (ESP), was developed by AECL (Canada) and jointly funded by NWMO (Canada), SKB (Sweden), Posiva Oy (Finland), and ANDRA (France), and since mid 2009 the thermal, hydraulic and mechanical evolution of the seal has been constantly monitored. The evolution of the type of seal being monitored in the ESP is of relevance to repository closure planning by demonstrating the functionality of shaft seals. Although constructed in a crystalline rock medium, the results of the ESP are relevant to the performance of seals in a variety of host rock types.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.686

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 integrity0.0000.000
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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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 designSimulation or modeling
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

Citations7
Published2014
Admission routes2
Has abstractyes

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