MétaCan
Menu
Back to cohort
Record W2051703960 · doi:10.1520/cca10489j

Measurement of the Alkali Content of Concrete Using Hot-Water Extraction

2002· article· en· W2051703960 on OpenAlexaff
M-A Bérubé, J Frenette, Michel Rivest, Daniel Vézina

Bibliographic record

VenueCement Concrete and Aggregates · 2002
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsMinistry of Transportation of OntarioHydro-QuébecUniversité Laval
Fundersnot available
KeywordsAlkali metalAlkali–aggregate reactionRepeatabilityAlkali–silica reactionWater contentGrindingReactivity (psychology)Extraction (chemistry)Aggregate (composite)ReproducibilityChemistryMaterials scienceMineralogyChromatographyComposite materialOrganic chemistryGeotechnical engineeringGeology

Abstract

fetched live from OpenAlex

Abstract The knowledge of the active- or soluble-alkali content of concrete is useful in the diagnosis and prognosis of alkali-aggregate reactivity (AAR). A method often used for determining this content is hot-water extraction from ground concrete samples. This method was applied to 17 aggregates and 8 concretes incorporating aggregates presenting different degrees of alkali-silica or alkali-carbonate reactivity. The following conclusions can be drawn: (1) a correction must be made to take account for the alkalies released by the aggregates in the test; (2) using cold water rather than hot water has no significant effect on the results; (3) grinding to <160 μm appears more appropriate than <80 μm (lower amount of alkalies released by the aggregates); (4) the soluble-alkali content progressively decreases as alkali-silica reaction (ASR) progresses, which indicates that a significant part of alkalies, progressively incorporated in the reaction products from ASR, is not leached in the test; (5) the repeatability from one series of tests to another and the reproducibility from one laboratory to another appear relatively poor; (6) the use of a control concrete with a known soluble-alkali content may greatly improve the repeatability and the reproducibility of the method.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.012
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.072
GPT teacher head0.240
Teacher spread0.168 · 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 teacher head, 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

Citations31
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueCement Concrete and AggregatesSame topicConcrete and Cement Materials ResearchFrench-language works237,207