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Record W2997572292 · doi:10.3390/min10010035

Thermal Disturbances in Permafrost Due to Open Pit Mining and Tailings Impoundment

2019· article· en· W2997572292 on OpenAlexafffundabout
Ahmad Booshehrian, Richard Wan, Grant Su

Bibliographic record

VenueMinerals · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsCanadian Nuclear Safety CommissionUniversity of Calgary
FundersCanadian Nuclear Safety CommissionAreva
KeywordsPermafrostTailingsImpervious surfaceGeologyExcavationGroundwaterMining engineeringEnvironmental scienceHydrology (agriculture)Geotechnical engineeringOceanography

Abstract

fetched live from OpenAlex

The paper is concerned with thermal disturbances in continuous permafrost due to open pit mining and tailings impoundment in the cold regions of Northern Canada. Numerical simulations were conducted to investigate issues of thermal regime changes and permafrost degradation in both the short term and the long term in connection with the proposed Kiggavik project in Nunavut, Canada. The results of numerical simulations indicate that no open talik would form below the East Zone and Center Zone pits during the estimated mining and milling operation period of 14 years, although a thin thawed zone would develop surrounding the open pits. For the Main Zone pit where the excavation would break through permafrost, the open talik remains following an extended operation period of 25 years with a 5 to 30 m thawed zone along the pit side walls. In the long term, with a plausible climate change scenario of 5 °C increase in the mean annual ground surface temperature during the next 100 years, the permafrost surrounding the in-pit tailings management facilities (TMFs) would reduce greatly in about 500 years. However, an approximately 40 m thick permafrost layer would remain on the top of the TMFs, which is impervious and would prevent any tailings pore water from migrating upward to reach the ground surface.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.994

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)0.0070.000

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 head0.031
GPT teacher head0.258
Teacher spread0.227 · 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.

Study designObservational
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

Citations2
Published2019
Admission routes3
Has abstractyes

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