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Record W4400116447 · doi:10.1016/j.compgeo.2024.106549

Investigating the controls of ice-wedge initiation and growth using XFEM

2024· article· en· W4400116447 on OpenAlexafffund
Gabriel Karam, Mehdi Pouragha, Stephan Gruber

Bibliographic record

VenueComputers and Geotechnics · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPermafrostIce wedgeLandformGeologyCrackingWedge (geometry)Geotechnical engineeringSoil waterArcticUltimate tensile strengthGeomorphologySoil scienceMaterials scienceGeometryComposite materialMathematics

Abstract

fetched live from OpenAlex

Ice-wedges are periglacial landforms that develop as a result of thermal contraction-cracking in continuous permafrost regions, which appear as polygonal networks on the ground surface. Given their complex thermo-mechanical loading history, very few related numerical models have so far been developed. In this study, 2-D eXtended finite element simulations are employed to represent the formation process of ice-wedges and to investigate the effect of select environmental controls on crack initiation and growth. Seventeen combinations of soil type and temperature–time series are used in four case studies addressing model testing, the permafrost stress regime, the freezing volumetric expansion of porewater, and a new remeshing process introduced to simulate ice-wedge growth over multiple years. The model testing shows good agreement with field observations from the Arctic and demonstrates the ability of the modelling procedure to reproduce the salient features of thermal contraction-cracking. The permafrost stress regime is found to be strongly affected by soil type and climate, with coarse-grained soils and cold climates leading to higher tensile stresses than fine-grained soils and warm climates. Higher tensile stress are also predicted for saturated soils due to the freezing volumetric expansion of porewater.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
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 head0.048
GPT teacher head0.247
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

Citations5
Published2024
Admission routes2
Has abstractyes

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