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Record W2924186078 · doi:10.1002/nag.2912

A depth average SPH model including <b><i>μ</i></b>(<i>I</i>) rheology and crushing for rock avalanches

2019· article· en· W2924186078 on OpenAlexaboutno aff
A. Longo, Manuel Pastor, Lorenzo Sanavia, Diego Manzanal, Miguel Martín Stickle, Chuan Lin, Ángel Yagüe, Saeid Moussavi Tayyebi

Bibliographic record

VenueInternational Journal for Numerical and Analytical Methods in Geomechanics · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaMinisterio de Asuntos Económicos y Transformación Digital, Gobierno de España
KeywordsRheologyFractalSmoothed-particle hydrodynamicsGeologyGeotechnical engineeringMechanicsFragmentation (computing)LawStatistical physicsPhysicsMathematicsThermodynamicsComputer scienceMathematical analysis

Abstract

fetched live from OpenAlex

Summary Classical depth‐integrated smoothed particle hydrodynamics (SPH) models for avalanches are extended in the present work to include a μ ( I )− rheological model enriched with a fragmentation law. With this improvement, the basal friction becomes grain distribution dependent. Rock avalanches, where grain distribution tends to change with time while propagating, are the appropriate type of landslide to apply the new numerical proposal. The μ ( I )− rheological models considered in the present work are those of Hatano and Gray, combined with two different fragmentation laws, a hyperbolic and a fractal‐based law. As an application, Frank avalanche, which took place in Canada in 1903, is analyzed under the scope of the present approach, focusing in the influence of the rheological and fragmentation laws in the evolution of the avalanche.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.675
Threshold uncertainty score0.461

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 head0.034
GPT teacher head0.366
Teacher spread0.332 · 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
GenreMethods

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

Citations27
Published2019
Admission routes1
Has abstractyes

Explore more

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