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Record W2765649358

Consolidation and atmospheric drying of fine oil sand tailings : Comparison of blind simulations and field scale results

2016· article· en· W2765649358 on OpenAlexfundaboutno aff
Philip J. Vardon, Y. Yao, Leon A. van Paassen, A.F. van Tol

Bibliographic record

VenueResearch Repository (Delft University of Technology) · 2016
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
FundersShell Canada
KeywordsTailingsConsolidation (business)Oil sandsOverburdenGeotechnical engineeringEnvironmental scienceVoid ratioComputer simulationGeologyEngineeringMaterials scienceMetallurgyAsphalt
DOInot available

Abstract

fetched live from OpenAlex

This paper presents a comparison between blind predictions of field tests of atmospheric drying of mature fine tailings (MFT) presented in IOSTC 2014 and field results. The numerical simulation of the consolidation and atmospheric drying of selfweight consolidating fine material is challenging and requires significant knowledge of the material, climate and the interaction between the two. This paper presents the outcome of a study which developed a numerical model, undertook material characterization and predicted the behaviour of full scale field tests undertaken in Shell Canada’s Muskeg River Mine near Fort McMurray, Alberta. The blind predictions were published in IOSTC 2014. A comparison between the observed and simulated behaviour in terms of settlement and void ratio yields a number of conclusions regarding the model: (i) all of the major observed features can be predicted by the numerical model; (ii) the quantification of the behaviour is well represented; (iii) due to the fast initial consolidation, the amount of material recorded as being deposited was underestimated; (iv) significant shear strength development requires a void ratio reduction which either requires a significant overburden or atmospheric drying.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.264

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 head0.017
GPT teacher head0.255
Teacher spread0.238 · 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 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

Citations0
Published2016
Admission routes2
Has abstractyes

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