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Beaching angles and evolution of stack geomoetry for thickened tailings - a review

2011· review· en· W2767075635 on OpenAlexafffund
Paul Simms, Paul Williams, Timothy Fitton, Gordon McPhail

Bibliographic record

VenuePaste/˜Pœaste · 2011
Typereview
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsCarleton University
FundersBarrick Gold Corporation
KeywordsStack (abstract data type)TailingsFootprintDeposition (geology)Selection (genetic algorithm)RealisationComputer scienceGeologyGeotechnical engineeringCivil engineeringEngineeringMaterials scienceArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

The reliable prediction or management of beach slope and stack geometry is integral to realisation of the potential benefits of thickened tailings technology. Stack geometry not only controls the storage capacity for a given footprint, but also strongly influences post-deposition geotechnical and geoenvironmental performance. Unfortunately, accurate prediction in design has proved difficult. However, there has been significant improvement in understanding the behaviour of tailings after they exit the pipe, and a number of methods for beach slope prediction have been proposed and developed. This paper reviews a selection of these methods, and examines their applicability to different deposition scenarios. Some recommendations are made to assist engineers and operators to achieve a given stack geometry.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.042
GPT teacher head0.261
Teacher spread0.219 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations9
Published2011
Admission routes2
Has abstractyes

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