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Record W2322603337 · doi:10.1061/40885(215)4

Bringing Science to an Art: A Decade of Shotcrete Research at the University of British Columbia

2006· article· en· W2322603337 on OpenAlexaffabout
Nemkumar Banthia, Sidney Mindess

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsShotcreteDurabilityProcess (computing)Computer scienceForensic engineeringConstruction engineeringEngineeringCivil engineering

Abstract

fetched live from OpenAlex

Shotcrete, produced via the dry-mix or the wet-mix processes, is fast becoming a material of choice for repair, rehabilitation, slope stabilization and ground support. With substantial advances both in the materials technology and high yield shotcreting pumps, shotcrete is fast shunning its image as a weak, inefficient material of low quality. The purpose of this paper is to summarize the research carried out at the University of British Columbia in the last decade or so. The topics covered include process optimization, mechanical properties and durability. Fiber reinforcement has been a topic of particular interest and this is covered in a greater detail. The paper also highlights novel areas of applications.

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)0.001
metaresearch head score (Gemma)0.003
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: Review · Consensus signal: Review
Teacher disagreement score0.411
Threshold uncertainty score0.826

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0070.005
Scholarly communication0.0070.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0160.002

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 head0.021
GPT teacher head0.256
Teacher spread0.235 · 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
GenreReview

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
Published2006
Admission routes2
Has abstractyes

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