MétaCan
Menu
Back to cohort

A fast method to find smooth economic envelopes for block and panel caving mines

2023· article· en· W4378363187 on OpenAlex
Francisco Saavedra, Nelson Morales, Gonzalo Nelis, René Gómez

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.

Bibliographic record

VenueResources Policy · 2023
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsEnvelope (radar)SmoothnessConstraint (computer-aided design)Block (permutation group theory)Computer scienceLimit (mathematics)Mathematical optimizationValue (mathematics)MathematicsGeometryMathematical analysis

Abstract

fetched live from OpenAlex

The first step in the planning of a mine is the determination of the economic envelope, which is the volume that encapsulates the mine layout and maximizes the total economic value; thus, it has a considerable impact in the design and planning of the mining project. The most common approach for calculating the economic envelope of block and panel caving mines is to determine the best envelope for each possible floor and then chose the est from manually. This method is very fast and effective but may generate footprints that are difficult to design and operate as they do not consider any geometrical constraint of the shape of the envelope that are relevant, for example, to limit abutment stress. In this paper, we present a novel method that incorporates geometrical constraints related to the connectivity and smoothness of the outline of the envelope, and the continuity and smoothness of the height of the draw columns. Our method is based on the same approach used to compute the ultimate pit, i.e., the economic envelope of open pit mines . Therefore, it is very efficient and ensures to find the optimal solution for the constraints that are set. We test the method, comparing the economic envelopes of the de facto approach and our proposed approach and show that the geometries obtained are more favorable geometries and generate economic values similar to or better than those calculated using the standard approach. That is, the method improves the geometry of the economic envelope without loss in value.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.572

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 head0.030
GPT teacher head0.288
Teacher spread0.258 · 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