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Record W2885748483 · doi:10.36487/acg_repo/711_9

Planning Considerations for Deep Level Mining at Perseverance Mine

2007· article· en· W2885748483 on OpenAlexaff
David Gaudreau, Iain Thin, A.T. Le Bron K. Haile

Bibliographic record

VenueDeep mining · 2007
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsComputer scienceMining engineeringData scienceGeology

Abstract

fetched live from OpenAlex

Given the high stress and poor rock mass conditions found at the Perseverance Mine, appropriate mine engineering and planning techniques are necessary, amongst other mine operational aspects, to perform sustainable mining at current and projected depths. This paper describes the processes and techniques relevant to mine planning and the geotechnical engineering considerations used at Perseverance in the context of deep mining in the Agnew-Wiluna Greenstone Belt. Perseverance Mine produces nickel sulphide ore using sub-level caving and long-hole stoping, and is wholly owned by BHP-Billiton Nickel West. The planning efforts relating to the current sub-level caving operation and the proposed future transition to block caving at a depth in excess of one kilometre are discussed.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.066
GPT teacher head0.268
Teacher spread0.202 · 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 designNot applicable
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

Citations2
Published2007
Admission routes1
Has abstractyes

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