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Record W4402615786 · doi:10.1080/17480930.2024.2400920

An effective strategy for stacking and reclaiming iron ore piles

2024· article· en· W4402615786 on OpenAlexaff
Aldrin Gustavo Martins, Mohammad Mahdi Badiozamani, Hooman Askari-Nasab, Paulo Santos Assis, Marcone Jamilson Freitas Souza

Bibliographic record

VenueInternational Journal of Mining Reclamation and Environment · 2024
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsUniversity of Alberta
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsIron oreMining engineeringGeologyEngineeringGeographyArchaeology

Abstract

fetched live from OpenAlex

This study examines the impact of the size of homogenised ore stockpiles on the feed rate of concentration-ore plants and the inter-pile grade variability. An integrated simulation optimisation tool is used to incorporate the dynamic characteristics of this process. Applying the discrete event simulation technique in this study made it possible to analyse the losses due to the lack of homogenised piles in the ore yard. Three scenarios from a Brazilian mine were explored. The results report a production gain of 3.85% in the scenario with the smallest ore pile size compared to the scenario with the largest pile size.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.259
Teacher spread0.244 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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