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Record W2027311501 · doi:10.1590/0370-44672014670171

Potvin stability graph applied to brazilian geomechanic environment

2014· article· en· W2027311501 on OpenAlexaboutno aff
Michel Melo, Cláudio Lúcio Lopes Pinto, José lldefonso Gusmão Dutra

Bibliographic record

VenueRem Revista Escola de Minas · 2014
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsStopingGraphStability (learning theory)Context (archaeology)Mining engineeringComputer scienceGeologyEngineeringTheoretical computer scienceMachine learning

Abstract

fetched live from OpenAlex

Empirical methods should be restricted to situations similar to the circumstances used for their development. The Stability Graph Method, first proposed by Mathews et al. (1981) and later modified by Potvin (1988), was based on data from Canadian and Australian mines. Therefore, there is no evidence of their use in the Brazilian geomechanical context. The Stability Graph Method is intended to design a stable open stope in mines using the Sublevel Stoping Mining method. The present study investigates the applicability of the methods in Sublevel Stoping Brazilian mines. This is a contribution to the validation of the use of the methods in the Brazilian geomechanical context.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.185
Teacher spread0.178 · 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.

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

Citations6
Published2014
Admission routes1
Has abstractyes

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