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Record W2975387804

Identification of Critical Factors Contributing to Increased Demand on Ground Support Elements at LaRonde Mine

2019· article· en· W2975387804 on OpenAlexaff
G. Sasseville, Martin Grenon, Philippe Morissette

Bibliographic record

VenueCorpus Université Laval (Université Laval) · 2019
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsAgnico Eagle (Canada)Université Laval
Fundersnot available
KeywordsIdentification (biology)Risk analysis (engineering)BusinessEnvironmental science
DOInot available

Abstract

fetched live from OpenAlex

The design of a ground support system must be sufficiently robust to hold, retain, and reinforce
\nthe excavations throughout its service life. The operational, geological, and geomechanical
\nproperties of the surrounding rockmass are known to impact the short- and long-term behavior of
\nground support systems, yet these impacts have not been fully quantified. In order to quantitatively
\nassess the influence of various parameters on increased demand on ground support elements, a
\nlarge database was created that collates historical rock support information: type, installation date,
\nand behavior over time of an entire mine sector (18.5 km of drift). Findings demonstrated that the
\nexcavation span, rock quality designation surrounding the excavation, and the excavation orientation
\nrelative to the foliation appear to be the critical factors controlling the demand on ground support
\nelements.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score1.000

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 head0.008
GPT teacher head0.195
Teacher spread0.187 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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