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Record W4416200161 · doi:10.1680/jmacr.25.00165

Suitability of lagoon fly ash for use in alkali-activated binder mortar mixes

2025· article· en· W4416200161 on OpenAlexfundno aff
Jacek Kwasny, Marios Soutsos

Bibliographic record

VenueMagazine of Concrete Research · 2025
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsFly ashLoss on ignitionMortarPortland cementParticle sizeSiloChemical composition

Abstract

fetched live from OpenAlex

The use of lagoon fly ash (LFA) was investigated as a precursor for alkali-activated/geopolymer concretes. The chemical composition, particle size distribution, mineralogy and morphology of LFA obtained from Kilroot power station (KLFA) were evaluated and compared with those of silo-stored FA from the same site (KFA). Geopolymer mortar mixes made with KLFA and KFA were compared. To determine the variability in the chemical composition of the KLFA, samples were taken from different locations during a ‘geological’ survey of the Kilroot power station’s lagoon in Northern Ireland. The samples of KLFA and KFA had similar chemical composition, minerology and particle size distribution. However, large agglomerated particles (lumps) of FA were found in the KLFA. The strengths of the mortar mixes made with KLFA, in the processed (dried and milled) and unprocessed states, were comparable to those of the silo fly ash (KFA) mixes. Most KLFA samples collected during the survey had a lower content of silicon and/or higher loss on ignition than the KFA, which would potentially adversely affect properties of concrete, if used as a partial replacement for Portland cement. KLFA is, however, suitable to use as a precursor for alkali-activated binders with little (e.g. screening/sieving through a 2 mm sieve) or no processing prior its use.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.027
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.065
GPT teacher head0.352
Teacher spread0.287 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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