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Record W2808425688 · doi:10.36487/acg_repo/563_7

Thickening of Oil Sands Composite Tailings

2005· article· en· W2808425688 on OpenAlexafffund
Rick Chalaturnyk, J. D. Scott, George N. Wong, Kwok-Hung Leung

Bibliographic record

VenuePaste/˜Pœaste · 2005
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
FundersSyncrude
KeywordsTailingsArithmetic underflowOil sandsThickeningEnvironmental scienceGeotechnical engineeringGeologyWaste managementMaterials scienceEngineeringComposite materialMetallurgy

Abstract

fetched live from OpenAlex

The current oil sands tailings management strategy is to cyclone the tailings stream and combine the cyclone underflow with cyclone overflow which has been allowed to settle for several years in a tailings pond. Gypsum is added to this mixture and a nonsegregating tailings called Composite Tailings (CT) is produced with a solids content of about 60% and a fines content of 20%. The CT is pumped to a disposal area where it is allowed to consolidate under self weight. This paper reports on trials to further increase the solids content of CT using a thickener. A research thickener, 1 m diameter with a 1 m depth, was used to investigate thick- ening the CT to a solids content sufficient to support reclamation methods. The thickener is equipped with pore pressure transducers down the side of the thick- ener and pore pressure and total stress transducers in the base of the thickener to fully evaluate the geotechnical processes occurring in the thickener. The pressure and stress transducers in conjunction with sampling allow total stress and effec- tive stress profiles to be determined at any time during the thickening test proce- dure. 118 Paste 2005, Santiago, Chile Thickening of Oil Sands Composite... Chalaturnyk, R.J. et al.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.849

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 head0.005
GPT teacher head0.177
Teacher spread0.172 · 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

Citations0
Published2005
Admission routes2
Has abstractyes

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