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Record W2953920677 · doi:10.1080/0305215x.2019.1624739

A planning approach for polymetallic mines using a sublevel stoping technique with pillars and ultimate stope limits

2019· article· en· W2953920677 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

VenueEngineering Optimization · 2019
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStopingEngineeringPlan (archaeology)Linear programmingInteger programmingMining engineeringCopper mineMathematical optimizationGeologyMathematicsCopper

Abstract

fetched live from OpenAlex

A widely used unsupported underground mining technique is sublevel stoping, in which portions of ore-body within certain size constraints are extracted. In this article, a sequential approach is proposed to solve the sublevel determination problem, which is part of development and infrastructure planning, and the stope layout planning problem for polymetallic sublevel stope mining with pillars. First, a new algorithm is proposed to determine the sublevels, which focuses on minimizing the development costs while maintaining access to the profitable portions of the ore-body. Then, the stope layout is planned between the sublevels. A new mixed-integer linear programming formulation for determining the ultimate stope limits is introduced. A case study is conducted on a copper–molybdenum mine to demonstrate the proposed approaches. The results show that the output of the stope layout plan is within the optimal mining limits, which confirms the validity of the approach.

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.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.156
Threshold uncertainty score0.821

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 head0.016
GPT teacher head0.204
Teacher spread0.188 · 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