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Record W2736500113 · doi:10.1080/14749009.2017.1296669

Interpreting the results of <i>in situ</i> pull tests on Friction Rock Stabilizers (FRS)

2017· article· en· W2736500113 on OpenAlexaffabout
Luke Nicholson, John Hadjigeorgiou

Bibliographic record

VenueMining Technology Transactions of the Institutions of Mining and Metallurgy · 2017
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRock boltEngineeringGeotechnical engineeringProbabilistic logicQuality assuranceQuality (philosophy)Rock mass classificationMining engineeringGeologyComputer science

Abstract

fetched live from OpenAlex

Friction Rock Stabilizers (FRS) are widely used in underground hard rock mines. An important element of Quality Control and Assurance practice is the use of in situ pull tests. Individual mines undertake in situ pull tests to demonstrate that suitable installation conditions exist for different reinforcement elements, and whether the selected bolts comply with the required standards. This paper reports on a review of 533 pull tests on FRS bolts in six underground hard rock mines in the Sudbury basin. It provides a critical review of factors that influence the performance of FRS bolts. The results are analyzed to provide design values for FRS that can be used as input to deterministic and probabilistic design. The paper concludes with a discussion on the practical implications of the results.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.620
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.019
GPT teacher head0.243
Teacher spread0.224 · 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 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

Citations7
Published2017
Admission routes2
Has abstractyes

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