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Volume change behavior of cement-based repair materials labelled as shrinkage-compensating, shrinkage-compensated or nonshrink

2022· article· en· W4307104054 on OpenAlexaff
Benoı̂t Bissonnette, Laurent Molez, Pierre-Vincent Certain, Charles Lamothe, Marc Jolin, Richard Gagné

Bibliographic record

VenueMATEC Web of Conferences · 2022
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsUniversité de SherbrookeUniversité Laval
Fundersnot available
KeywordsShrinkageCementitiousVolume (thermodynamics)CementMaterials scienceComputer scienceComposite material

Abstract

fetched live from OpenAlex

The labels shrinkage-compensating, shrinkage-compensated and nonshrink found in the technical documentation of many proprietary repair materials are all intended in principle to describe systems that exhibit no or little net contraction as a result of shrinkage. In practice, however, these terms are of limited significance in the selection of repair materials without appropriate test data on time-dependent volume changes. This paper provides clarifications on the dimensional behavior of shrinkage-compensating materials and uses experimental findings to emphasize the shortcomings in the information provided in the data sheets of many repair materials labelled as such. In view of a more effective and reliable use of cementitious shrinkage-compensating repair materials, recommendations are made to improve and uniformize the content of the technical data sheets.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.037
GPT teacher head0.260
Teacher spread0.223 · 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
Published2022
Admission routes1
Has abstractyes

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