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Record W2094540483 · doi:10.1139/l08-093

Performance of Pakistani volcanic ashes in mortars and concrete

2008· article· en· W2094540483 on OpenAlexaffvenue
Amjad Naseer, Abdul Jabbar, Akhtar Naeem Khan, Qaisar Ali, Zakir Hussain, Jahangir Mirza

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsHydro-Québec
FundersNorth-West UniversityUniversity of Engineering and Technology, Lahore
KeywordsMortarMaterials sciencePortland cementPozzolanComposite materialCementPozzolanaAggregate (composite)Metallurgy

Abstract

fetched live from OpenAlex

Two Pakistani volcanic ashes, VA1 (as is and calcined) and VA2 (as is), were incorporated into mortar cubes, concrete cylinders, and concrete beams as a partial substitute for ordinary Portland cement (OPC), and were studied in detail. The X-ray diffraction patterns showed that both ashes possessed crystalline as well as amorphous phases. The pozzolanic activity index (PAI) of VA1 at 7 d was below 75%, whereas it was 80% at 28 d. The pozzolonic activity indices in OPC mortars containing VA2 were much higher than those for VA1, both at 7 and 28 d. In mortar cubes and concrete cylinders, approximately the same compressive strengths were observed in samples containing 100% cement as in those incorporating a 10% replacement of cement by either VA1 or VA2. Mortar cubes soaked in 5% sodium sulphate solution demonstrated consistently improved resistances to sulphate attack as ash content increased in the mortar. Similar results were also observed in water absorption tests. Modulus of rupture of all concrete beams decreased as the levels of VA1 and VA2 increased from 10% of OPC content to 40%. Rupture moduli were higher for VA1 than for VA2 at all cement replacement levels.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 head0.010
GPT teacher head0.189
Teacher spread0.179 · 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

Citations12
Published2008
Admission routes2
Has abstractyes

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