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Record W2526153181 · doi:10.1139/cgj-2016-0399

Depth of penetration of lubricant fluids and water in Queenston shale of southern Ontario

2016· article· en· W2526153181 on OpenAlexaffvenueabout
Hayder Mohammed Salim Al-Maamori, M. Hesham El Naggar, S. Micic

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsWestern University
Fundersnot available
KeywordsOil shalePenetration (warfare)GeologyGeotechnical engineeringPenetration testLubricantPetroleum engineeringSwellingMaterials scienceComposite materialEngineering

Abstract

fetched live from OpenAlex

Queenston shale is a rock formation located in southern Ontario in Canada that exhibits time-dependent deformation behaviour induced by water penetration. Microtunnelling is an efficient technique that can be used to construct pipelines and tunnels in Queenston shale. In this technique, lubricant fluids, such as bentonite solution and polymer solution, are utilized, which can have a significant impact on the time-dependant deformation (i.e., swelling) of Queenston shale within their depth of influence. A test procedure was developed in this research to investigate the depth of penetration of lubricant fluids and water in Queenston shale. These fluids were applied under pressure, similar to that used in the microtunnelling process, on Queeenston shale specimens. The depth of penetration of these fluids into the test specimens was monitored with time. It was concluded that the penetration depth varied for the various fluids considered. Water had the deepest penetration into Queenston shale. Based on the observed results, an equation is developed to predict the depth of penetration of various fluids in the Queenston shale mass. Results of this research can assist in evaluating the depth of influence of various fluids in Queenston shale and the associated swelling zone can be predicted.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.928

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 head0.006
GPT teacher head0.171
Teacher spread0.165 · 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

Citations7
Published2016
Admission routes3
Has abstractyes

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