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Record W2692741810

Selection of ground support for mining drives based on the Q-System

2016· article· en· W2692741810 on OpenAlexaffabout
Yves Potvin, John Hadjigeorgiou

Bibliographic record

VenueUWA Profiles and Research Repository (University of Western Australia) · 2016
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSelection (genetic algorithm)Computer scienceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

As part of the Ground Support Systems Optimisation (GGSO) project undertaken by the Australian Centre for Geomechanics and collaborators, a comprehensive review of the current ground support design approaches implemented at mine sites throughout Australia and Canada was conducted. The review has shown that most mines rely primarily on the Grimstad and Barton (1993) chart to select their initial ground support patterns and standard ground support practices.<br/>There are a number of issues and limitations relating to the application to mining problems of this empirical technique, originally developed for civil engineering. These limitations are to a large degree attributable to the absence of mining case studies in the database and changes in ground support practices since the development of the original guidelines. As part of the GSSO research project a significant database of ground support practices used at mine sites has recently been developed. New empirical guidelines, mainly based on mining data collected from Australia and Canada, are proposed to be used at the feasibility stage, as a first pass design and for subsequent optimisation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.202

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 head0.072
GPT teacher head0.278
Teacher spread0.206 · 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 designBench or experimental
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

Citations12
Published2016
Admission routes2
Has abstractyes

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