MétaCan
Menu
Back to cohort

An ASR exposure site after 30 years – Chemical composition of hydrates and ASR products

2025· article· en· W4414654343 on OpenAlexafffund
Maxime Ranger, Andreas Leemann, Benoît Fournier

Bibliographic record

VenueCement and Concrete Research · 2025
Typearticle
Languageen
FieldMaterials Science
TopicGraphite, nuclear technology, radiation studies
Canadian institutionsGeological Survey of Canada
FundersNational Research Council CanadaHydro-QuébecFonds de recherche du QuébecUniversité de SherbrookeUniversité Laval
KeywordsCementitiousChemical compositionAggregate (composite)Alkali–aggregate reactionRange (aeronautics)

Abstract

fetched live from OpenAlex

Concrete specimens containing two aggregates susceptible to alkali-silica reaction (ASR) and various supplementary cementitious materials (SCMs) have been exposed outdoors since 1992. After 30 years of expansion monitoring, some specimens were cored to conduct an in-depth analysis of their condition. The chemical compositions of the hydrates and the ASR products were quantified with SEM-EDS. All SCMs increased the Si/Ca of the C-(A)-S-H, but the extent varied depending on their composition, amount and degree of reaction. Larger amounts of SCMs resulted in lower degrees of reaction. The ASR products inside aggregate particles of non-boosted concrete mixtures were crystalline. Their Ca/Si was in the range 0.21–0.24, independent of the binder and the aggregate types. The (Na + K)/Ca was usually in the range 0.31–0.35. The Na/K varied more, correlating with the respective ratios in the binder. The Al content of the ASR products inside aggregate particles was not influenced by the presence of Al-rich SCMs.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.314
Teacher spread0.292 · 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 designObservational
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

Citations5
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueCement and Concrete ResearchSame topicGraphite, nuclear technology, radiation studiesFrench-language works237,207