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Record W2806887985 · doi:10.1139/cgj-2017-0653

Quantifying influence of drilling additional boreholes on quality of geological model

2018· article· en· W2806887985 on OpenAlexaffvenue
Marie-Hélène Fillion, John Hadjigeorgiou

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsHudbay Minerals (Canada)Lawson Health Research Institute
Fundersnot available
KeywordsBoreholeGeologyGeotechnical engineeringDrillingRock mass classificationDrillGeotechnical investigationMining engineeringEngineering

Abstract

fetched live from OpenAlex

Geotechnical stability analysis in open-pit mines requires access to a representative geotechnical model. The confidence level in the collected geotechnical data influences slope design. This paper investigates the influence of the number of boreholes, drilled to collect geological information, on the quality of one component of the geotechnical model, the geological model. The number of boreholes influences the number of rock core samples collected for the identification of rock type, and the definition of geotechnical domains and their boundaries within the rock mass. A challenge in the definition of the geotechnical domains is the determination of the drill hole density that minimizes the variation in the interpreted geological model from the actual rock mass. To quantify the influence of the drill hole density, boreholes are simulated in the most recently updated geological model for three mine sites. The simulated drill hole density is increased progressively until the variation of the interpreted section, compared with the original section, is minimized. A classification strategy was developed to determine the complexity level for each geotechnical domain. Furthermore, a series of empirical quantitative guidelines are presented prescribing the minimum drill hole density per domain complexity, while limiting variations from the actual rock mass.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.444

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 head0.061
GPT teacher head0.278
Teacher spread0.217 · 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 designSimulation or modeling
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

Citations10
Published2018
Admission routes2
Has abstractyes

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