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Record W4311620417 · doi:10.1080/25726668.2022.2151112

Quality assurance considerations for friction rock stabilizers

2022· article· en· W4311620417 on OpenAlexafffund
John Hadjigeorgiou, Steven J. Thorpe, Kevin M. Cole

Bibliographic record

VenueMining Technology Transactions of the Institutions of Mining and Metallurgy · 2022
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsQuality assuranceQuality (philosophy)GeologyGeotechnical engineeringForensic engineeringMining engineeringEngineeringOperations managementPhysics

Abstract

fetched live from OpenAlex

Friction rock stabilizers (FRS) are routinely used to provide rock reinforcement to excavations in hard rock. Since the expiration of the original patent taken out in 1977, FRS are now available from multiple suppliers. The expectation that all FRS are equivalent, however, is not necessarily accurate. There are inherent variations in the design configuration, material chemistry/properties, and consequently in performance. In this context, a full traceability from steel grade and heat treatment to final product is critical. This paper reports the results of a QA/QC investigation aiming to identify the material properties for three equivalent FRS from the same supplier. A series of mechanical and accelerated corrosion tests highlighted the differences in these rockbolts. This work provides a template for a more comprehensive quality assurance programme to ensure compliance of FRS. This is critical when rockbolts, due to global supply chain constraints, are outsourced from multiple sources worldwide.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.036
GPT teacher head0.257
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2022
Admission routes2
Has abstractyes

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Same venueMining Technology Transactions of the Institutions of Mining and MetallurgySame topicTunneling and Rock MechanicsFrench-language works237,207