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Record W4200547975 · doi:10.3390/app112411614

Geometry-Based Preliminary Quantification of Landslide-Induced Impulse Wave Attenuation in Mountain Lakes

2021· article· en· W4200547975 on OpenAlexaffabout
Andrea Franco, Barbara Schneider‐Muntau, Nicholas J. Roberts, John J. Clague, Bernhard Gems

Bibliographic record

VenueApplied Sciences · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAttenuationGeologyLandslideImpulse (physics)ShoreDissipationAmplitudeGeometryGeomorphologyPhysicsMathematicsOpticsClassical mechanics

Abstract

fetched live from OpenAlex

In this work, a simple methodology for preliminarily assessing the magnitude of potential landslide-induced impulse waves’ attenuation in mountain lakes is presented. A set of metrics is used to define the geometries of theoretical mountain lakes of different sizes and shapes and to simulate impulse waves in them using the hydrodynamic software Flow-3D. The modeling results provide the ‘wave decay potential’, a ratio between the maximum wave amplitude and the flow depth at the shoreline. Wave decay potential is highly correlated with what is defined as the ‘shape product’, a metric that represents lake geometry. The relation between these two parameters can be used to evaluate wave dissipation in a natural lake given its geometric properties, and thus estimate expected flow depth at the shoreline. This novel approach is tested by applying it to a real-world event, the 2007 landslide-generated wave in Chehalis Lake (Canada), where the results match well with those obtained using the empirical equation provided by ETH Zurich (2019 Edition). This work represents the initial stage in the development of this method, and it encourages additional research and modeling in which the influence of the impacting characteristics on the resulting waves and flow depths is investigated.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.023
GPT teacher head0.252
Teacher spread0.229 · 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 designBench or experimental
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

Citations14
Published2021
Admission routes2
Has abstractyes

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