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Record W2921968771 · doi:10.3390/min9030172

Mine Backfilling in the Permafrost, Part II: Effect of Declining Curing Temperature on the Short-Term Unconfined Compressive Strength of Cemented Paste Backfills

2019· article· en· W2921968771 on OpenAlexafffund
Mamert Mbonimpa, Parrein Kwizera, Tikou Belem

Bibliographic record

VenueMinerals · 2019
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCuring (chemistry)PermafrostCompressive strengthCementMaterials scienceComposite materialGeotechnical engineeringHumidityGeologyThermodynamics

Abstract

fetched live from OpenAlex

When cemented paste backfill (CPB) is used to fill underground stopes opened in permafrost, depending on the distance from the permafrost wall, the curing temperature within the CPB matrix decreases progressively over time until equilibrium with the permafrost is reached (after several years). In this study, the influence of declining curing temperature (above freezing temperature) on the evolution of the unconfined compressive strength (UCS) of CPB over 28 days’ curing is investigated. CPB mixtures were prepared with a high early (HE) cement and a blend of 80% slag and 20% General Use cement (S-GU) at 5% and 3% contents and cured at room temperature in a humidity chamber and under decreasing temperatures in a temperature-controlled chamber. Results indicate that UCS is higher for CPB cured at room temperature than under declining temperatures. UCS increases progressively from the stope wall toward the inside of the CPB mass. Under declines in curing temperature, HE cement provides better short-term compressive strength than does S-GU binder. In addition, the gradual decline in temperature does not appear to affect the fact that the higher the binder proportion, the greater the strength development. Therefore, UCS is higher for samples prepared with 5% than 3% HE cement. Findings are discussed in terms of practical applications.

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)0.001
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 score0.048
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.015
GPT teacher head0.222
Teacher spread0.207 · 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

Citations11
Published2019
Admission routes2
Has abstractyes

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