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Record W4367154902 · doi:10.36487/acg_repo/2355_55

A case study on the commingling of tailings and waste rock at a Brownfields open cast mine in Ghana

2023· article· en· W4367154902 on OpenAlexaff
Johan Boshoff, Louise McNab, Nathaniel Asifu Mensah

Bibliographic record

VenuePaste/˜Pœaste · 2023
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsBanff CentreGeomechanica (Canada)University of Alberta
Fundersnot available
KeywordsTailingsMining engineeringOpen-pit miningGeologyWaste managementMetallurgyEngineeringMaterials science

Abstract

fetched live from OpenAlex

Tailings management is multifaceted in its ultimate goal of zero harm to people and the environment, in line with the Global Industry Standard on Tailings Management (GISTM).Gold Fields is committed to conforming with the GISTM and achieving its environment, social and governance (ESG) priorities, of which one is investigating the feasibility of tailings innovation projects, such as the tailings moisture content reduction.There is significant mining industry interest in developing geo-stable tailings storage facilities, combining tailings and mine waste to form a geochemically and physically stable landform.However, while geo-stable materials are a topic of industry-wide interest, there is a lack of a sound knowledge base and testing protocols to assess, compare and validate the performance of different technical approaches across different mineralogical and operational situations.The paper presents the characterisation of material used to construct geo-stable tailings and waste rock management facilities and the performance of such facilities at the trial pad scale.The ultimate aim in the commingling journey at this site is to form single-footprint waste management systems with a reduced total footprint area compared to constructing separate tailings storage facilities and waste rock dumps.The material characteristics, operational environment, trial pad construction, placement and testing methods will be discussed as part of this initial step in the geo-waste journey.

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 score0.586
Threshold uncertainty score0.549

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 head0.057
GPT teacher head0.263
Teacher spread0.206 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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