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Record W4292607196 · doi:10.36487/acg_repo/2205_34

A monitoring strategy to assess the effectiveness of pillar wrapping

2022· article· en· W4292607196 on OpenAlexaff
Yoko Yanagimura, John Hadjigeorgiou

Bibliographic record

VenueCaving 2022: Fifth International Conference on Block and Sublevel Caving · 2022
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPillarBlock (permutation group theory)EngineeringStructural engineering

Abstract

fetched live from OpenAlex

Pillar wrapping using cable slings is an increasingly popular ground support strategy to control large deformations in block cave mines. Although installation can be relatively costly and labour intensive, pillar wrapping is attractive as it is more effective than other ground support options. This is due to the greater elongation capacity of cable slings compared to other ground support elements. At this time, however, the performance of pillar wrapping in block cave mines is mostly anecdotal and based on limited quantified performance data. This paper presents a data-driven monitoring strategy to assess the performance of pillar wrapping at the New Afton Mine. The mine currently employs cable slings to support the junctions between the production drives and drawpoints in the B3 mining area. In a field investigation, as part of the pillar wrapping strategy, instrumented cable bolts have been installed to directly monitor the strain and loads that develop in the pillar wrapping support system during the cave initiation and propagation. The analysis of the cable instrumentation data provides for a data-driven approach for understanding the performance of the cables used for pillar wrapping. An objective of this investigation is to provide quality data to aid the mine to make informed cost-benefit decisions in its ground support decisions.

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 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 score0.199
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.064
GPT teacher head0.282
Teacher spread0.217 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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