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Record W3097866668 · doi:10.1029/2020jc016465

Wave Generation Across a Continuum of Landslide Conditions From the Collapse of Partially Submerged to Fully Submerged Granular Columns

2020· article· en· W3097866668 on OpenAlexaff
Miguel Cabrera, Gustavo Pinzón, W. Andy Take, Ryan P. Mulligan

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsSubaerialDimensionless quantityAmplitudeGeologyWater columnLandslideMomentum (technical analysis)Range (aeronautics)MechanicsMesoscopic physicsFroude numberGeotechnical engineeringPhysicsSeismologyMaterials scienceOceanographyOptics

Abstract

fetched live from OpenAlex

Abstract Landslide generated tsunamis are a primary natural hazard to coastal communities and infrastructure, but the present state of knowledge is limited to the specific details of momentum transfer from the landslide to the water body for either subaerial or fully submerged conditions. Here we report a series of novel experiments and an analytical model that bridges this gap, by exploring the collapse of a granular column for a wide range of water depths. We show that the maximum seaward wave amplitude is governed by a single dimensionless ratio, the relative depth of submergence. Based on the experimental observations, we propose a continuous function that quantifies the maximum wave amplitude by considering the momentum flux from the initially vertical granular column to the initially still fluid. Predictions made using the momentum function are in good agreement with observations of the present study and with other experimental studies of granular column collapse at larger scales. The analytical model allows the prediction of maximum wave amplitude over the full range of submergence conditions from subaerial to partially submerged and fully submerged collapses, with potential applications for tsunami hazard assessment.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.086
GPT teacher head0.319
Teacher spread0.233 · 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 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

Citations42
Published2020
Admission routes1
Has abstractyes

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