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Record W4296709732 · doi:10.1139/cgj-2022-0052

Geotechnical performance of fine tailings in an oil sands pit lake

2022· article· en· W4296709732 on OpenAlexafffundvenueabout
Adedeji Dunmola, Robert A. Werneiwski, Dallas McGowan, Bill Shaw, David Carrier

Bibliographic record

VenueCanadian Geotechnical Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsBGC Engineering (Canada)Wildlife Habitat Canada (Canada)Conetec InvestigationsSyncrude (Canada)
FundersSyncrudeStrong
KeywordsGeotechnical engineeringConsolidation (business)Oil sandsTailingsGeologyTailings damContext (archaeology)AsphaltGeography

Abstract

fetched live from OpenAlex

Syncrude Canada Ltd.’s Base Mine Lake (BML), the first commercial-scale demonstration of an oil sands pit lake, was commissioned in December 2012, following in-pit deposition of fine tailings (FT) between 1995 and 2012. The geotechnical design basis for the FT in BML is that it will consolidate and densify over time, contributing to fines sequestration below the water cap. This paper presents the geotechnical performance of the FT in BML within the context of this geotechnical design basis. The FT has settled from 196.0 to 171.6 Mm 3 by 2019, with cumulative settlement varying spatially between 0.3 and 6.7 m. FT settlement is consistent with the expected self-weight consolidation as modeled by finite-strain non-linear consolidation theory, and is reflected as temporal increase in the profiles of solids content and effective stress. Sonar surveying, profile sampling, shear strength, and underwater photography show that the transition of geotechnical properties at the mudline becomes increasingly distinct over time. These observations support the geotechnical design basis for BML and indicate the fines continue to be sequestered below the water cap.

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 categoriesnone
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 score0.041
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.002
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 head0.009
GPT teacher head0.198
Teacher spread0.189 · 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

Citations8
Published2022
Admission routes4
Has abstractyes

Explore more

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