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Record W4402545768 · doi:10.36487/acg_repo/2465_49

Three-dimensional modelling of development intersections at the Eleonore mine

2024· article· en· W4402545768 on OpenAlexfundno aff
Gregory Turfan, Shahé Shnorhokian, Hani S. Mitri, Amanda Conley

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsnot available
FundersMcGill University
KeywordsComputer science

Abstract

fetched live from OpenAlex

Several ground control challenges are encountered during the narrow-vein mining of parallel orebodies in a complex geologic environment at the Eleonore mine. Mining-induced seismicity and related rock mechanics instability events often take place at vulnerable locations such as development intersections. Both static and dynamic ground support systems are used to control the effects of these events. The mining sequence followed is usually pyramidal and moves from bottom to top with regional pillars separating the different blocks. Numerical modelling is used to assess the stress redistributions based on the stope sequences being planned. In this paper a geometrically simplified 3D linear elastic model of the Eleonore mine is constructed for several levels, along with the drift and crosscut systems on L 860, L 830 and L 800. Rock mass properties used as model inputs are obtained from recent laboratory tests and core logs, as well as older studies conducted for the Eleonore mine. Calibration is conducted with boundary stresses being applied to obtain pre-mining magnitudes comparable to those measured in the field. A typical pyramidal sequence is implemented and locations within the development network where potential instability could take place are identified based on 1 and brittle shear ratio (BSR) thresholds. Based on these initial results a detailed 3D model is then constructed of potential vulnerable intersections. The outputs from the two models are compared and the impact of mining is assessed with respect to the mode of instability observed at these intersections.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.344

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 head0.031
GPT teacher head0.209
Teacher spread0.178 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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