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Record W3027286695 · doi:10.1080/19236026.2020.1730141

Statistical assessment of intact rock properties for two underground mining projects at Raglan Mine, Quebec, Canada

2020· article· en· W3027286695 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCIM Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsGlencore (Canada)Université Laval
Fundersnot available
KeywordsExcavationCompressive strengthGeotechnical engineeringMining engineeringRock mechanicsReliability (semiconductor)Ultimate tensile strengthUnderground mining (soft rock)GeologyEngineeringMaterials science

Abstract

fetched live from OpenAlex

ABSTRACT The design of underground mining excavations relies on geotechnical characterization of intact rock through laboratory testing. As mining project development progresses through prefeasibility to production stages, the reliability of estimates of rock mechanics properties needs to increase. However, it is challenging to determine the number of tests needed to adequately estimate intact rock properties at different development stages. This paper looks at the early stages of an underground mining project in the Canadian Arctic where two field and laboratory testing campaigns were conducted to evaluate intact rock tensile and uniaxial compressive strength. Results were statistically compared to target confidence levels associated with different stages of a mining project. The study provides insights for planning future field and laboratory testing campaigns. The methodology also provides a quantitative means to assess whether additional laboratory testing is needed to improve tensile and uniaxial compressive strength estimates.

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: Empirical · Consensus signal: none
Teacher disagreement score0.649
Threshold uncertainty score0.996

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 head0.022
GPT teacher head0.237
Teacher spread0.214 · 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