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Record W3015749781 · doi:10.1080/17480930.2020.1743035

Coupled effect of sulphate and temperature on the reactivity of cemented tailings backfill

2020· article· en· W3015749781 on OpenAlexaff
Zaid Aldhafeeri, Mamadou Fall

Bibliographic record

VenueInternational Journal of Mining Reclamation and Environment · 2020
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPortland cementTailingsReactivity (psychology)Curing (chemistry)CementChemistryMicrostructureDurabilityMaterials scienceMetallurgyComposite material

Abstract

fetched live from OpenAlex

The reactivity of sulphidic cemented paste tailings (CPT) is an indicator which can be used to assess their environmental performance and durability. However, the reactivity is influenced by several factors either alone or in combination with other factors, such as temperature, curing time, and initial sulphate content. In this paper, the combined effect of the initial sulphate content and curing temperature on the reactivity of CPT is experimentally investigated with oxygen consumption (OC) tests. Microstructural analyses of the CPT samples are also performed to understand the impact of the microstructure of the CPTs on their reactivity. The results show that regardless of the binder type, the reactivity of the CPT system is significantly dependent on the curing temperature and initial sulphate content and their interaction. As curing temperature and sulphate concentration increase, the reactivity increases (except for the CPT samples with 5,000 ppm of sulphate). Moreover, the CPT sample with high contents of sulphate (25,000 ppm) and cured at 50°C has the highest reactivity regardless of the type of binder. The addition of mineral admixtures as a partial substitute for Portland cement also has a significant role in reducing the reactivity as compared to samples made only with Portland cement.

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 score0.165
Threshold uncertainty score0.175

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)0.0000.000

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 head0.011
GPT teacher head0.193
Teacher spread0.182 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Mining Reclamation and EnvironmentSame topicTailings Management and PropertiesFrench-language works237,207