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Record W7157800997 · doi:10.15665/rp.v22i2.3512

Modeling and simulation of coal flow in a mine using discrete events

2024· article· es· W7157800997 on OpenAlexaff
Carlos Parra Ortega, Javier Mauricio García Mogollón, Jorge Luis Díaz Rodríguez

Bibliographic record

VenueJusticia Juris (La Universidad Autónoma del Caribe) · 2024
Typearticle
Languagees
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCoal miningFlow (mathematics)Modeling and simulationSimulation modelingMoment (physics)Work (physics)CoalDiscrete event simulation

Abstract

fetched live from OpenAlex

Today's industry requires increasingly accurate and faster results in analysis that lead to continuous improvement of its processes. Simulation is one of the tools that meets this need as it allows us to design models of a real system and carry out experiments with these models in order to understand their operation and evaluate different operating strategies. This work uses discrete event simulation as a method to analyze and simulate the flow of coal in an open pit coal mine from the moment it is extracted from the subsoil until its rail shipment, with a view to providing a system in which can evaluate different possible operating scenarios.

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 categoriesMeta-epidemiology (narrow)
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 score0.166
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.016
GPT teacher head0.256
Teacher spread0.240 · 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.

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
Published2024
Admission routes1
Has abstractyes

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