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Record W2599000272 · doi:10.1139/cgj-2016-0028

Geotechnical properties of polymer-amended tailings solvent recovery unit (TSRU) oil sands tailings

2017· article· en· W2599000272 on OpenAlexafffundvenue
Chloe L. Dean, Sumi Siddiqua, Deborah J. Roberts

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of British Columbia, Okanagan Campus
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTailingsOil sandsPyriteGeotechnical engineeringGeologyMining engineeringAsphaltGeochemistryMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Fine tailings from the tailings solvent recovery unit (TSRU) in the Athabasca oil sands are known to contain a relatively high pyrite content and a high residual hydrocarbon content, which may alter their geotechnical properties. Little is known about TSRU tailings properties and therefore the potential for subaerial deposition. The goal of this study was to investigate the geotechnical properties of untreated, polymer-amended, and sand-mixed TSRU tailings to provide information for the consideration of subaerial deposition and increase the general knowledge about these unique tailings. The polymer-amended tailings had more desirable properties for subaerial deposition, including a lower final void ratio, less energy required to desaturate, and higher compressibility, when compared with the untreated tailings. The sand-mixed samples enhanced these properties, but may pose issues for transportation. The mineralogy indicated that the polymer-amended TSRU tailings have a high-enough pyrite content for acid generation, which may pose environmental issues for subaerial deposition. Overall, TSRU tailings exhibited different geotechnical properties when compared with the well-studied mature fine tailings, highlighting the need for further studies to provide information for the management of TSRU tailings.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.203
Teacher spread0.186 · 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.

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

Citations10
Published2017
Admission routes3
Has abstractyes

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