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Record W2758643383 · doi:10.36487/acg_rep/1504_42_kam

Managing high-density tailings disposal – deposition, water management and closure considerations

2015· article· en· W2758643383 on OpenAlexaffabout
Shiu Kam, David Yaschyshyn, Nuri Hmidi

Bibliographic record

VenuePaste/˜Pœaste · 2015
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsGlencore (Canada)Golder Associates (Canada)
Fundersnot available
KeywordsTailingsDeposition (geology)Environmental scienceMining engineeringGeologyMaterials scienceMetallurgySediment

Abstract

fetched live from OpenAlex

There has been a growing interest in the surface disposal of high-density tailings in recent years. Such an interest is driven primarily by the need to conduct mining in a sustainable manner by minimising environmental and social impact, conserving water and improving tailings safety. Increasingly, more mines are considering thickened or paste tailings disposal in conjunction with paste tailings backfill to better utilise their infrastructure. Several factors have hindered the wider adoption of high-density surface tailings disposal technology. The ability to predict tailings behaviour in full scale is still relatively limited. There is also little experience in managing a high-density tailings facility under varied climatic conditions. A number of high capacity mines have also had production issues meeting the tailings consistency criterion for deposition leading to major reconfiguration of the disposal system due to poor field performance. Some of these uncertainties are being addressed through laboratory and field research with significant advances being made on our understanding on tailings rheology, transportation, mechanism of deposition and post deposition behaviour change of the tailings. High-density tailings facilities will require a greater degree of management than conventional slurry tailings. Not only are the tailings likely more variable, there are unique challenges associated with tailings deposition and water management. This paper discusses tailings deposition control and water management methods that have been successfully used at a number of high-density tailings facilities with special reference made to central cone deposition in the Kidd Metallurgical Site and perimeter deposition at the Musselwhite Mine. Both of these facilities are located in cold climatic regions in Canada with surplus precipitation. Contingency measures that have been adopted to reduce the risk of system upsets and non-performance, and closure considerations for these facilities are highlighted. Field experience has shown that high-density tailings disposal can offer unique opportunities for progressive rehabilitation and optimisation of the closure design.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.717

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 head0.012
GPT teacher head0.184
Teacher spread0.172 · 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
Published2015
Admission routes2
Has abstractyes

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