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Record W2806643586

Optimal Open Pit Design and Life of Mine Scheduling of the Bisha Volcanogenic Massive Sulphide Deposit

2018· article· en· W2806643586 on OpenAlexaboutno aff
Peter Eshun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyMining engineeringOpen-pit miningScheduleTonnageDrillingExcavationSmeltingEngineeringGeotechnical engineeringMetallurgyComputer science
DOInot available

Abstract

fetched live from OpenAlex

Bisha Mining Share Company (BMSC), Eritrea, is a subsidiary of Nevsun Resources of Canada. BMSC has completed an exploration work which discovered Volcanogenic Massive Sulphide (VMS) deposit containing commercial quantities of gold, silver, copper and zinc at Bisha. The objective of this paper is to design an optimal open pit and generate a Life of Mine (LOM) schedule for the profitable exploitation of the deposit. Thus, Net Smelter Return values were modelled using a script developed in Surpac. The deposit was optimised and an open pit was designed using MineSight software under the given geotechnical and economic parameters with a marginal tonnage deviation of 3%. A Life of Mine (LOM) schedule carried out in MineSched predicted a mine life of 10 years and defined the transitional periods for the exploitation of gold, copper and zinc deposits. It is recommended that the designed optimal pit and the LOM schedule should be used for the exploitation of the Bisha VMS deposit. Also, extensions to the primary massive sulphide mineralisation, including the Primary Zn Domain, should be tested with further drilling. This may have the effect of increasing the mineable reserve and extend the project life.

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 score0.146
Threshold uncertainty score0.245

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 head0.027
GPT teacher head0.232
Teacher spread0.205 · 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
Published2018
Admission routes1
Has abstractyes

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