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Percolation experiment across a 10-year-old interface between Opalinus Clay and Portland concrete

2023· article· en· W4367399278 on OpenAlexfundno aff
Ellina Bernard, Andreas Jenni, Nikolajs Toropovs, Urs Mäder

Bibliographic record

VenueCement and Concrete Research · 2023
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
FundersHorizon 2020 Framework ProgrammeAgence Nationale pour la Gestion des Déchets RadioactifsUniversité de FribourgEuropean CommissionNuclear Waste Management OrganizationNationale Genossenschaft für die Lagerung radioaktiver AbfälleSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsHydraulic conductivityMaterials sciencePortland cementPercolation (cognitive psychology)Pore water pressureChlorideGeotechnical engineeringPorosityComposite materialMineralogyGeologySoil scienceMetallurgyCementSoil water

Abstract

fetched live from OpenAlex

An interface sample between Portland concrete and Opalinus Clay with a contact time of 10 years recovered from a field experiment was investigated by SEM-EDX and X-ray CT. The concrete side showed a large chloride ingress from the claystone alongside a decalcification and an opening of the porosity. Additional XRD, TGA and leaching experiments of the concrete at few centimetres (∼5 cm) away from the interface confirmed the chloride ingress. The interface was then subjected to a long-term percolation experiment accompanied with repeated X-ray CT-scans. Injection of synthetic claystone pore water proceeded into the claystone-part of the sample, and through the concrete part, whereby the outflow was continuously sampled. The bedding joints that were partially desaturated rapidly saturated, while hydraulic conductivity steadily decreased to values similar to unaltered claystone. The analysis of the exfiltrating aliquots shed light on the advective/diffusive properties of water transport and multi-component solute transport.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.001
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 head0.080
GPT teacher head0.381
Teacher spread0.301 · 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 designBench or experimental
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

Citations4
Published2023
Admission routes1
Has abstractyes

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