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Record W4367155056 · doi:10.36487/acg_repo/2355_42

Rheology for deposition control and deposit failure risk analysis

2023· article· en· W4367155056 on OpenAlexafffund
Paul Simms, Xuexin Xia, A Patel, E Parent, Nicholas Beier, Gonzalo Zambrano-Narváez

Bibliographic record

VenuePaste/˜Pœaste · 2023
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsCarleton UniversityBanff CentreGeomechanica (Canada)University of Alberta
FundersCanada's Oil Sands Innovation AllianceBarrick Gold Corporation
KeywordsRheologyDeposition (geology)Materials scienceComputer sciencePetroleum engineeringGeologyComposite materialGeomorphology

Abstract

fetched live from OpenAlex

Application of rheology to the post-deposition behaviour of tailings is important for the design of the slope of the impoundment, control of layering for purposes of strength enhancement through desiccation, properly describing the mixing of different tailings streams and for dam breach consequence analysis. This paper aims to advance the understanding of tailings rheology in the post-deposition context to aid all of these applications. Firstly, thixotropy and its quantitative implications for beach slope and layer geometry are explored using a simple treatment. Secondly, method dependency in yield stress measurement is discussed, along with how such uncertainty can be practically handled by considering the appropriate stress path and timescale for a particular application. Finally, numerical simulation using a thixotropic rheology of channel flow down a beach and runout from a dam breach experiment, conducted in a centrifuge, is used to highlight the utility of advanced rheology to such problems. The paper uses data collected from published work on both hard rock and oil sands tailings over the last 15 years.

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 score0.385
Threshold uncertainty score0.535

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 head0.204
Teacher spread0.199 · 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
Published2023
Admission routes2
Has abstractyes

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