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Record W2406645891 · doi:10.26190/unsworks/25764

Sample size and sample strength effects on testing the performance of cable bolts

2016· article· en· W2406645891 on OpenAlexfundno aff
Hao Zhai, Paul Hagan, Danqi Li

Bibliographic record

VenueResearch Online (University of Wollongong) · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersUniversity of WollongongUniversity of New South WalesAustralian Coal Industry’s Research ProgramMcGill UniversityCommonwealth Scientific and Industrial Research OrganisationAustralasian Institute of Mining and Metallurgy
KeywordsSample (material)Sample size determinationStructural engineeringForensic engineeringEngineeringMathematicsStatisticsPhysics

Abstract

fetched live from OpenAlex

This paper presents the results of a study on the effect of test sample diameter on the peak load carrying capacity of cable bolts in varying conditions. It was previously found that peak load varies with the diameter of test sample up to 300 mm and further that confinement has a significant impact on load. In this study test samples were varied over a larger range of diameters up to 500 mm in test samples having strengths of 32 MPa and 66 MPa using a plain strand Superstrand cable bolt and a nutcage high capacity MW9 cable bolt. The test work confirmed that there are differences in anchorage performance in material of different strength between the two cable bolts and importantly that a 300 mm diameter sample size is required when comparing different types of cable bolts design.

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)0.013
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.013
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.237
Teacher spread0.215 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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