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Record W2211580485 · doi:10.1680/jgele.15.00129

Magnesium sulfate attack on clays stabilised by carbide slag- and magnesia-ground granulated blast furnace slag

2015· article· en· W2211580485 on OpenAlexafffund
Yaolin Yi, Canhao Li, S. Liu, Fei Jin

Bibliographic record

VenueGéotechnique Letters · 2015
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of ChinaUniversity of Alberta
KeywordsGround granulated blast-furnace slagMagnesiumGypsumSulfateSlag (welding)MetallurgyMaterials scienceCalcium silicate hydrateCement

Abstract

fetched live from OpenAlex

Carbide slag (CS)– and reactive magnesia (MgO)–activated ground granulated blast furnace slag (GGBS) were used to stabilise soft clay subjected to accelerated magnesium sulfate (MgSO4) attack. The results indicated that the CS–GGBS-stabilised clay had a higher resistance to magnesium sulfate attack than magnesia–GGBS-stabilised clay; this was contrary to findings from a previous study subjected to sodium sulfate attack. The formation of gypsum and the decomposition of calcium silicate hydrate (CSH) occurred in both stabilised clays after the magnesium sulfate attack, which is primarily responsible for the deterioration. Since the magnesia–GGBS-stabilised clay had a lower pH buffering capacity and calcium/silicon ratio of CSH than CS–GGBS-stabilised clay, it was more susceptible to magnesium sulfate attack.

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 score0.003
Threshold uncertainty score0.005

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 head0.025
GPT teacher head0.244
Teacher spread0.219 · 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

Citations61
Published2015
Admission routes2
Has abstractyes

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