MétaCan
Menu
Back to cohort
Record W4367155230 · doi:10.36487/acg_repo/2355_56

Case study: the impact of tailings properties on conveying system designs

2023· article· en· W4367155230 on OpenAlexaff
Christopher W. Olsen, Kenneth Rahal

Bibliographic record

VenuePaste/˜Pœaste · 2023
Typearticle
Languageen
FieldEngineering
TopicBelt Conveyor Systems Engineering
Canadian institutionsBanff CentreGeomechanica (Canada)University of Alberta
Fundersnot available
KeywordsTailingsComputer scienceEnvironmental scienceMining engineeringGeologyMetallurgyMaterials science

Abstract

fetched live from OpenAlex

Mine wastes, specifically tailings, are commonplace in mining operations and most mines dispose of their tailings wastes in wet impoundment structures. Consequently, the failure of wet impoundment structures is one of the most significant environmental liabilities for mining operations and recent failures have highlighted the perils of this type of tailings disposal strategy. Likewise, water scarcity continues to be a growing concern at many mines globally, specifically within arid regions. Risk mitigation priorities along with water resource conservations are steering the mining industry’s waste management of tailings away from wet impoundment and towards dewatered tailings and dry stack disposal. The handling of dewatered tailings is most efficiently performed with the operation of automatic conveyance systems and the deposition of the tailings achieved by mobile conveyor stacking systems. Lab testing and analysis have identified that mine waste tailings characteristics vary widely between mine samples due mostly to the ore’s mineral composition, particle size distribution, and moisture content. Evaluating the mine site’s tailings material samples for their conveyability and measuring their change in surcharge angle is the key to understanding how the tailings react at different moisture levels while being transported along the length of an overland conveyor. The results of the conveyability tests are used for the design and strategy for the material handling and waste disposal stacking systems. This paper will present case studies of multiple tailings samples, from various mine sites, at specifically determined moisture levels. During the conveyor simulation tests, the samples were measured and recorded for the initial angle of repose, surcharge angle, and material density. This paper aims to demonstrate that there are often significant differences between tailings samples’ physical and dynamic properties and how that relates to the parameters needed for accurate conveyor engineering 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.079
GPT teacher head0.260
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venuePaste/˜PœasteSame topicBelt Conveyor Systems EngineeringFrench-language works237,207