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Record W2263857721 · doi:10.1680/envgeo.13.00079

Geotechnical properties of centrifuged oil sand fine tailings

2014· article· en· W2263857721 on OpenAlexafffund
John Owolagba, Shahid Azam

Bibliographic record

VenueEnvironmental Geotechnics · 2014
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Regina
KeywordsTailingsGeotechnical engineeringDegree of saturationDewateringHydraulic conductivityConsolidation (business)ShrinkageCentrifugeSuctionAtterberg limitsOil sandsSaturation (graph theory)Permeability (electromagnetism)Volume (thermodynamics)Materials scienceSoil waterEnvironmental scienceGeologyWater contentSoil scienceComposite materialMetallurgyAsphalt

Abstract

fetched live from OpenAlex

The main objective of this study was to determine the geotechnical properties of centrifuged oil sand fine tailings for surface deposition. A material with 60% solids (e = 1·5) was obtained using a bench-scale centrifuge. The fine-grained material (52% clay fraction) with a moderate water adsorption capacity (w l = 40% and w p = 20%) indicated an air entry value of 1000 kPa and a residual suction of 30 000 kPa. The correct way of representing the soil water characteristic curve for tailings was found to be the one based on degree of saturation along with simultaneous volume change measurements. The tailings mainly dewatered during normal shrinkage while remaining saturated whereas volume reduction was negligible beyond the shrinkage limit. Likewise, dewatering under an effective stress of 160 kPa was found to be 67% along with a compression index of 0·36. The saturated hydraulic conductivity measured 3 × 10−10 m/s that decreased marginally during consolidation and rapidly (10−10 to 10−18 m/s) due to suction application.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.006
GPT teacher head0.156
Teacher spread0.150 · 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 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

Citations13
Published2014
Admission routes2
Has abstractyes

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