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Towards mining schedule optimisation constrained by geomechanics

2014· article· en· W2623169724 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueDeep mining · 2014
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsLaurentian University
FundersOntario Centres of Excellence
KeywordsGeomechanicsExcavationInduced seismicityScheduleRock mass classificationGeologyConstraint (computer-aided design)Geotechnical engineeringMining engineeringStability (learning theory)Process (computing)EngineeringComputer scienceSeismologyMachine learning

Abstract

fetched live from OpenAlex

This paper reports work-in-progress that has the aim of incorporating geotechnical constraints on the optimisation of schedules of underground mine excavation activities. Optimal mine schedules are those for which the net present value (NPV) is maximised under a given financial model. The approach aims to consider the visco-elastic behaviour of rock when analysing the stability of excavations for constraint formulation. Changes in deviatoric stress around multiple, sequential openings or excavations are mapped and are shown to be influenced by both changes in the time between excavation events and the sequence of excavating events. The results of this analysis are presented in this paper. The approach is predicated on a causality principle for mine seismicity which requires a time dependent response (visco-elastic or possibly visco-elasto-plastic) of the rock mass. This is potentially difficult to conceive for rock masses at great depth, but is nevertheless evidenced by mine seismicity records from deep level mining operations, some of which are reviewed in this paper. The output of these geotechnical sequencing studies will ultimately be cast as constraints on a mine schedule optimisation process.

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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: none
Teacher disagreement score0.636
Threshold uncertainty score0.802

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 head0.012
GPT teacher head0.210
Teacher spread0.198 · 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