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Record W2945365283 · doi:10.1080/17480930.2019.1595903

Influence of freeze–thaw cycles on mechanical responses of cemented paste tailings in surface storage

2019· article· en· W2945365283 on OpenAlexaff
Wenbin Xu, Mingrui Han, Li Pan

Bibliographic record

VenueInternational Journal of Mining Reclamation and Environment · 2019
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversity of Ottawa
FundersChina Scholarship Council
KeywordsTailingsCementGeotechnical engineeringEnvironmental scienceCuring (chemistry)Materials scienceGeologyMining engineeringMetallurgyComposite material

Abstract

fetched live from OpenAlex

Surface cemented paste tailings disposal (SPD) has become a useful practice in many mines around the world. The method is a sustainable tailings management that returns tailings to fill the surface cavities and subsidence caused by mining operations, thereby maximising the safety, efficiency and environment of tailings management. This study was conducted to manifest the mechanical responses of the SPD cover layer induced by freezing and thawing cycle (FTC). It shows that with an increase of FTCs, lots of microcracks are formed, scaled and spalled on the sample's surface. The uniaxial compressive strength (UCS) and ultrasonic pulse velocity (UPV) of SPD specimens decrease with increasing FTCs. The reduction of the specimen with a higher cement-to-tailing ratio and longer curing age is smaller than that with lower cement content. The logarithm equation can be available to estimate the UCS values of SPD with the measured data of UPV. The results may be helpful to better understand not only SPD for sustainable tailings management but also the enhancement of the rehabilitation of subsidence in mine regions.

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)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.011
GPT teacher head0.211
Teacher spread0.200 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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