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Mining of orebodies under shear loading Part 2 – failure modes and mechanisms

2014· article· en· W2026638842 on OpenAlexaff
Fidelis T. Suorineni, Juma Mgumbwa, Peter Kaiser, Denis Thibodeau

Bibliographic record

VenueMining Technology Transactions of the Institutions of Mining and Metallurgy Section A · 2014
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsStantec (Canada)Ministry of Energy, Northern Development and MinesLaurentian UniversityCentre for Excellence in Mining Innovation
Fundersnot available
KeywordsExcavationShear (geology)GeologyGeotechnical engineeringSloughingShear stressMining engineeringMaterials scienceComposite materialPetrology

Abstract

fetched live from OpenAlex

Part 1 of the paper defined shear loaded orebodies and showed through case histories that both pillars and excavations are at elevated risks of failure when mining these orebodies. Part 2 of the paper presents new knowledge on the behaviour of pillars and excavations when mining such orebodies. Numerical modelling is used to understand the behaviours of these structures in the orebodies. It is established that pillars in shear suffer confinement loss compared to their equivalents under pure compression. The confinement loss increases with increasing shear loading in pillars with width: height ( W : H )≥1. For pillars with W : H ≥1, the Lunder and Pakalnis (1997) empirical pillar design chart should be used with caution. For excavations in eccentrically loaded orebodies, passive and active high stress envelopes are created in the excavation process. The combined effect of the active high stress and tension zones often results in excavation surface sloughing.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.199
Teacher spread0.186 · 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 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

Citations44
Published2014
Admission routes1
Has abstractyes

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