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Record W4396615485 · doi:10.1061/9780784485415.026

Recent Observations on Self-Hardening Slurries in Slurry Trenching

2024· article· en· W4396615485 on OpenAlexaff
Nathan Coughenour, Daniel Ruffing, Jeffrey Evans

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsKensington Health
Fundersnot available
KeywordsSlurryCoringPortland cementHardening (computing)Geotechnical engineeringCementCivil engineeringEngineeringGeologyMaterials scienceMechanical engineeringDrillingMetallurgyComposite materialEnvironmental engineering

Abstract

fetched live from OpenAlex

Self-hardening slurries such as cement-bentonite and slag-cement-bentonite have been increasingly used in the installation of subsurface vertical barriers since the 1970s. In this paper, the authors build on previously reported lessons learned from the use of self-hardening slurries in vertical barriers to highlight recent developments and observations related to the applicability, specifications, installation, and testing of self-hardening slurry trenches. These lessons are based on direct observations from recent field applications. Lessons from this recent field experience offer new insight and reinforce existing knowledge. Topics addressed include the switch from type I/II Portland cement to type IL Portland cement, observations from field sampling and testing including coring and in situ permeability testing, comparison of strength and hydraulic conductivity results from mix design samples, wet-grab samples and core samples, discussion of changes in recent specifications, and updates on constructability considerations.

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)0.001
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.026
GPT teacher head0.253
Teacher spread0.227 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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