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Record W4401546907 · doi:10.1080/17480930.2024.2381904

An application of simultaneous stochastic optimisation on an open-pit copper mining complex with supply, recovery, and market uncertainties

2024· article· en· W4401546907 on OpenAlexafffund
Yi Jiang, Roussos Dimitrakopoulos

Bibliographic record

VenueInternational Journal of Mining Reclamation and Environment · 2024
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsMcGill University
FundersAngloGold AshantiCanada Research Chairs
KeywordsCopper mineOpen-pit miningCopperStochastic modellingComputer scienceEngineeringEnvironmental scienceMining engineeringMathematicsMaterials scienceMetallurgyStatistics

Abstract

fetched live from OpenAlex

A mining complex is an integrated value chain that consists of multiple mines, stockpiles, processors, waste dumps, and tailing facilities, and where the in-situ materials mined are transformed into products sold to customers. Simultaneous stochastic optimisation provides a strategic mine planning framework that incorporates all components of a mining complex into a single mathematical representation, capitalising on the synergies between components while considering uncertainty. An application of this optimisation framework at a coper mining complex demonstrates that the incorporation of material supply, market demand, and processing recovery uncertainties into the modelling leads to production schedules that yield a higher NPV and metal recovery.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
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)0.0030.000

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 head0.014
GPT teacher head0.245
Teacher spread0.231 · 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 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

Citations7
Published2024
Admission routes2
Has abstractyes

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