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Record W7128526340 · doi:10.5281/zenodo.18595522

Managing the Integrity of Mine Cage Conveyance using Fault Tree Analysis

2021· article· W7128526340 on OpenAlexaff
Efe Peter Iyomi, Olutayo Opeyemi Ogunmilua, Isaque Moyses Guimaraes

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typearticle
Language
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsVale (Canada)
Fundersnot available
KeywordsFault tree analysisCriticalityFailure mode, effects, and criticality analysisUnderground mining (soft rock)Fault (geology)Tree (set theory)Lead (geology)

Abstract

fetched live from OpenAlex

The mine cage conveyance is a critical piece of equipment in an underground mine as they are considered as the lifeline for transporting miners and equipment to an underground mine. Considering the criticality of the mine cage conveyance, managing its integrity for the safe operation has become a priority as failures resulting from their operation can become catastrophic. This study shows how fault tree analysis can be used to analyze failures associated with the mine cage conveyance while showing the various branches of events that can lead to such failures and their order of criticality for the various associated components.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.496
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.000

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 head0.053
GPT teacher head0.255
Teacher spread0.202 · 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
Published2021
Admission routes1
Has abstractyes

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