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Record W2921262579 · doi:10.3390/min9030165

Mine Backfilling in the Permafrost, Part I: Numerical Prediction of Thermal Curing Conditions within the Cemented Paste Backfill Matrix

2019· article· en· W2921262579 on OpenAlexafffund
Fabrice Beya, Mamert Mbonimpa, Tikou Belem, Li Li, Ugo Marceau, Patrick Kalonji, Mostafa Benzaazoua, Serge Ouellet

Bibliographic record

VenueMinerals · 2019
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsAgnico Eagle (Canada)Polytechnique MontréalUniversité du Québec en Abitibi-TémiscamingueGolder Associates (Canada)
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsPermafrostGeotechnical engineeringCuring (chemistry)MultiphysicsThermalHeat transferMaterials scienceRock mass classificationGeologyComposite materialMechanicsFinite element methodThermodynamicsEngineeringStructural engineering

Abstract

fetched live from OpenAlex

The mechanical behavior of cemented paste backfill (CPB) in permafrost regions may depend on the thermal curing conditions. However, few experimental data are available for calibrating and validating numerical models used to predict these conditions. To fill this gap, a three-dimensional (3D) laboratory heat transfer test was conducted on CPB placed in an instrumented barrel and cured under a constant temperature of −11 °C. Results were used to calibrate and validate a numerical model built with COMSOL Multiphysics®. The model was then used to predict the evolution of the temperature field for CPB cured under the thermal boundary conditions for a backfilled mine stope in the permafrost (at −6 °C). Numerical results indicated that the CPB temperature gradually decreased with time such that the entire CPB mass was frozen about five years after stope backfilling. However, the permafrost equilibrium temperature of −6 °C was not reached throughout the entire CPB mass even after 20 years of curing. In addition, the evolution of the temperature field in the permafrost rock showed that the thickness of the thawed portion reached about 1 m within 120 days. Afterwards, the temperature continues to drop over time and the thawed portion of the permafrost refreezes after 365 days.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.413

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 head0.017
GPT teacher head0.217
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations15
Published2019
Admission routes2
Has abstractyes

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