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Record W2332757466 · doi:10.1680/jphmg.15.00006

Physical modelling of oil sands tailings consolidation

2015· article· en· W2332757466 on OpenAlexaffabout
Amarebh R. Sorta, David C. Sego, Ward Wilson

Bibliographic record

VenueInternational Journal of Physical Modelling in Geotechnics · 2015
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsUniversity of AlbertaCoanda Research and Development Corporation (Canada)
Fundersnot available
KeywordsCentrifugeConsolidation (business)Geotechnical engineeringTailingsOil sandsSlurryGeologyPore water pressureEngineeringMaterials scienceAsphaltComposite material

Abstract

fetched live from OpenAlex

The self-weight consolidation of oil sands tailings and kaolinite slurry is modelled using a new geotechnical beam centrifuge located at the University of Alberta (Geomechanical Reservoir Engineering Facility (GeoREF) centrifuge). The centrifuge is used to create a prototype-effective stress regime in the model, and centrifuge tests are conducted at different initial compositions at acceleration levels of 40 to 100g. The objectives of the centrifuge tests are to study the long-term consolidation behaviour, derive consolidation parameters, verify modelling procedures and to evaluate time-domain reflectometry application for solids content profile monitoring during centrifuge testing. The interface settlement, pore pressure and solids content profiles of the consolidating materials are monitored in-flight. Large strain consolidation parameters are derived from centrifuge tests and compared with parameters determined from a large strain consolidometer. The paper describes the modelling aspect as well as the instrumentation and monitoring of centrifuge tests. The centrifuge results are presented, discussed and compared using results from large strain numerical models.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.296
Teacher spread0.249 · 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 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

Citations12
Published2015
Admission routes2
Has abstractyes

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