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Record W4390948735 · doi:10.1680/jphmg.22.00015

Development of a small-scale geotechnical centrifuge

2024· article· en· W4390948735 on OpenAlexaffabout
Amarebh R. Sorta, Neville Dubash, Benny Moyls, Scott Webster, Oladipo Omotoso

Bibliographic record

VenueInternational Journal of Physical Modelling in Geotechnics · 2024
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsSuncor Energy (Canada)Coanda Research and Development Corporation (Canada)
Fundersnot available
KeywordsCentrifugeConsolidation (business)TailingsGeotechnical engineeringEngineeringCivil engineeringEnvironmental sciencePetroleum engineeringMaterials science

Abstract

fetched live from OpenAlex

Oil sand fluid fine tailings in northern Alberta have very poor water-release characteristics and require many decades to fully consolidate under their own weight. Generally, physical and/or chemical treatments are necessary to improve the consolidation behaviour and manage the tailings in an economical and environmentally acceptable manner. The effect of each treatment method is commonly evaluated, in part, by measuring the short- and long-term consolidation behaviour of treated samples. Geotechnical beam centrifuges, large-strain consolidation apparatus and geocolumns are typically used to measure the consolidation properties. However, these methods require many months to complete or are expensive and difficult to deploy in the field. A small-scale geotechnical centrifuge suitable for use in both research environments and laboratories at industrial sites was developed to study and/or monitor various treatment and disposal options in a short time. This paper presents the motivation for developing a small-scale centrifuge, the components and features of the centrifuge and the assortment of tests completed to validate the apparatus and testing methods. The validation tests proved that the small-scale centrifuge produces comparable results with existing methods while reducing the cost and time required to evaluate the consolidation performance of different tailings treatment options.

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: none
Teacher disagreement score0.540
Threshold uncertainty score0.584

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 integrity0.0000.001
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 head0.017
GPT teacher head0.245
Teacher spread0.228 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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