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Record W2038007663 · doi:10.1520/jai100432

Effect of Aggregate Particle Size on Determining Alkali-Silica Reactivity by Accelerated Tests

2006· article· en· W2038007663 on OpenAlexaff
Lu Deng, Benoît Fournier, P.E. Grattan-Bellew

Bibliographic record

VenueJournal of ASTM International · 2006
Typearticle
Languageen
FieldMaterials Science
TopicGraphite, nuclear technology, radiation studies
Canadian institutionsInstitute for Biological SciencesNatural Resources Canada
Fundersnot available
KeywordsAggregate (composite)Materials scienceAlkali–silica reactionParticle sizeReactivity (psychology)Particle (ecology)Alkali metalComposite materialChemical engineeringChemistryGeologyOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Abstract For assessing the rock applicability of accelerated tests for alkali-aggregate reactivity and the effect of aggregate particle size on determining alkali reactivity of concrete aggregates in accelerated tests, experimental studies on microstructure and expansion behaviors of Potsdam sandstone and a Norwegian quartzite were conducted in Concrete Prism Test and in various accelerated tests, i.e., Accelerated Mortar Bar Test, Chinese Autoclave Method, and Chinese Accelerated Mortar Bar Test. Results indicate that, in comparison with Concrete Prism Test results, the alkali expansivity of both rocks are generally underestimated in these accelerated tests. It is mainly attributed to the use of a large proportion of very fine aggregate particles in which the original microtexture characteristic of rocks was lost during sample preparation. The effects of microtexture and the particle size of aggregate on reasonable prediction of alkali expansivity of aggregates in concrete by accelerated tests were discussed.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.281
Teacher spread0.271 · 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

Citations16
Published2006
Admission routes1
Has abstractyes

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Same venueJournal of ASTM InternationalSame topicGraphite, nuclear technology, radiation studiesFrench-language works237,207