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Record W2296120341 · doi:10.1139/cgj-2015-0300

Influence of lubricant fluids on swelling behaviour of Queenston shale in southern Ontario

2016· article· en· W2296120341 on OpenAlexaffvenueabout
Hayder Mohammed Salim Al-Maamori, M. Hesham El Naggar, S. Micic, K. Y. Lo

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsWestern University
Fundersnot available
KeywordsSwellingOil shaleGeotechnical engineeringSwellBentoniteGeologySlurryLubricantPetroleum engineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The feasibility of using the microtunnelling technique to install pipelines through Queenston shale of southern Ontario is being investigated. In the microtunnelling technique, lubricant fluids — such as bentonite slurry and polymer solution — are used to facilitate excavation during installation of the pipeline sections. In this regard, a comprehensive testing program was performed to investigate the time-dependent deformation behaviour of Queenston shale considering lubricant fluids used in construction. The free swell test, semi-confined swell test, and the null swell test were utilized to perform this study. Results of 144 tests are presented and the variation of swelling characteristics of Queenston shale in lubricant fluids and in water is discussed briefly. The swelling model suggested by Lo and Hefny in 1996 was adopted to develop the swelling envelopes of Queenston shale in lubricant fluids and water in both horizontal and vertical directions with respect to the rock bedding. In comparison to swelling in fresh water, the study revealed that the polymer solution has substantially reduced the swelling of Queenston shale in all directions, while the bentonite solution was less efficient in reducing the swelling of Niagara Queenston shale, and has a slight negative influence on the swelling (i.e., increased swelling) of Milton Queenston shale.

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 score0.932
Threshold uncertainty score0.889

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)0.0000.000

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 head0.006
GPT teacher head0.182
Teacher spread0.176 · 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

Citations6
Published2016
Admission routes3
Has abstractyes

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