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Record W2806461238 · doi:10.1061/9780784481608.021

Soil-Bentonite Slurry Trench Cutoff Wall Longevity

2018· article· nds· W2806461238 on OpenAlexaff
Daniel Ruffing, Jeffrey Evans, Nathan Coughenour

Bibliographic record

VenueIFCEE 2018 · 2018
Typearticle
Languagends
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsKensington Health
Fundersnot available
KeywordsTrenchSlurryGeotechnical engineeringEnvironmental scienceBentoniteShieldLongevityGroundwaterGeologyEnvironmental engineeringMaterials science

Abstract

fetched live from OpenAlex

Soil-bentonite (SB) slurry trench cutoff walls are widely used for subsurface containment of contaminants and groundwater control. In many cases, particularly for environmental containment applications, the walls are intended to serve as a permanent barrier with a life expectancy measured in decades. The compatibility of these barriers with the environment is considered during most design studies but a comprehensive review of the longevity of these walls is absent from the literature. This paper identifies key longevity issues, summarizes published literature to date and describes the current practice related to the longevity of soil-bentonite slurry trench cutoff walls. Principle issues potentially affecting long-term performance include compatibility between the backfill and the groundwater, impact of wetting/drying in the zone of the fluctuating water table, long-term property changes due to secondary compression of the backfill, potential for desiccation of the near-surface zone of the barrier wall and the potential for hydraulic fracturing due to the relatively low state of effective stress. The paper concludes with the authors’ summary opinions and suggestions for future research.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.216
Teacher spread0.205 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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