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Record W4399797076 · doi:10.1139/cgj-2024-0044

3D large deformation modeling of the 2020 Gjerdrum quick clay landslide

2024· article· en· W4399797076 on OpenAlexvenueno aff
Quoc Anh Tran, Agnete Rogstad, Ivan Depina, Fabricio Fernández, Gebray Habtu Alene, Gustav Grimstad, Steinar Nordal

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
FundersStatens vegvesenEuropean Commission
KeywordsGeotechnical engineeringGeologyLandslideDeformation (meteorology)Discontinuous Deformation AnalysisEngineeringFinite element methodStructural engineering

Abstract

fetched live from OpenAlex

A quick clay slide in Gjerdrum, Norway, occurred at 4 a.m. on 30th December 2020, killing 10 people and destroying houses, roads, and other infrastructures. Approximately, 1.35 million cubic meters of clay were released, a large volume liquefied, and debris was transported almost 2 km that the slide was initialized in downstream. An investigation following the slide determined that the slide was initialized in a 30 m high slope after 2-to-2.5 m vertical erosion in a small creek running along the toe of the slope. After the initiation, the slide developed retrogressively in the order of 500 m backward and sideways over a period of about 2 min. A conventional geotechnical slope stability analysis explains the initial slide. However, more advanced numerical tools are needed to simulate the retrogressive mechanism and the debris flow. The aim of the paper is to demonstrate a 3D material point method model to capture some of the mechanisms involved from initiation until the debris comes to rest and how this method can be used to reproduce and study the processes involved in large deformation landslides.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.126
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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 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 routes1
Has abstractyes

Explore more

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