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Mechanistic Model for One-Dimensional Consolidation Behavior of Nonsegregating Oil Sands Tailings

2008· article· en· W2075957057 on OpenAlexafffund
R.C.K. Wong, Brian Mills, Y. B. Liu

Bibliographic record

VenueJournal of Geotechnical and Geoenvironmental Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsGolder Associates (Canada)University of Calgary
FundersShell CanadaUniversity of Calgary
KeywordsTailingsOil sandsConsolidation (business)Geotechnical engineeringAsphaltGeologyEnvironmental sciencePetroleum engineeringMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Thermal-chemical-hydraulic separation process is used to extract bitumen from surface mined oil sands ores. Huge amounts of oil sands fine tailings are produced from the extraction process. The most fundamentally challenging issue facing the geo-environmental community is containment, long-term storage, and volume reduction of oil sands fine tailings. One of the fine tailings disposal techniques is to homogenize fine tailings with coarse tailings forming nonsegregating tailings (NST). NST exhibits enhanced performance in consolidation and strength, and reduction in water retention as compared to fine tailings. This paper examines one-dimensional consolidation behavior of NST with varying fine and coarse tailings compositions. A mechanistic model based on theory of mixture is developed and proposed to predict consolidation behavior of NST. This model is demonstrated to achieve an optimum design of NST for accelerated consolidation rate and water recovery.

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 score0.078
Threshold uncertainty score0.875

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 head0.013
GPT teacher head0.188
Teacher spread0.175 · 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

Citations29
Published2008
Admission routes2
Has abstractyes

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