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Record W4412990200 · doi:10.56952/arma-2025-0173

Slope monitoring plan and risk management process- Case study: Chadormalu open pit mine

2025· article· en· W4412990200 on OpenAlexaff
Soroush Ali Madadi, Saeed Mahmoudi Janaki, Abolfazl Rezaeipour, Yousef Abolfazlzadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsBritish Columbia Centre on Substance UseIron Ore Company (Canada)
Fundersnot available
KeywordsOpen-pit miningPlan (archaeology)Mining engineeringProcess (computing)Risk managementComputer scienceEnvironmental scienceGeologyBusinessOperating system

Abstract

fetched live from OpenAlex

ABSTRACT: A comprehensive understanding of the risks and the implementation of suitable control operations are crucial for the effective management of unstable walls in large open pit mines. The Chadormalu mine is divided into five domains. Domain 1 is of paramount importance, as reducing the slope is not a viable option. The wall is situated on a substantial portion of the ore, in conjunction with structures and power towers. The analysis, which attributes the properties of the tectonized zone to the unstable regions, yields results that closely align with the actual conditions. Consequently, the implementation of the TARP program and monitoring system effectively addressed the instability. This strategy facilitated mitigation with minimal associated costs and risks. Nevertheless, the potential loss of 2 million tons of waste remains a concern. To address this, a numerical analysis of the finite element was conducted, focusing exclusively on the primary structures, according to the Moher-Columb criterion. A comprehensive examination of the area revealed wedge-shaped collapses, as well as plane ruptures and block toppling failure.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.403

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 head0.021
GPT teacher head0.276
Teacher spread0.255 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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