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Record W2052390428 · doi:10.1134/s0001433814050053

Numerical modeling of the long surface waves scattering for the 2011 Japan tsunami: Case study

2014· article· en· W2052390428 on OpenAlexaff
E. A. Kulikov, I. Fine, O. I. Yakovenko

Bibliographic record

VenueIzvestiya Atmospheric and Oceanic Physics · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsNorth Pacific Marine Science Organization
FundersU.S. Geological Survey
KeywordsGeologyDissipationScatteringTsunami waveEquipartition theoremSeismologyRayleigh scatteringRayleigh distributionMeteorologyGeophysicsPhysicsOptics

Abstract

fetched live from OpenAlex

The March 11, 2011, megaquake caused a catastrophic tsunami recorded throughout the Pacific. This paper presents an analysis of the sea-level records obtained from deep-water tsunami meters (DART and NEPTUNE). To evaluate the effect of the sea-level oscillations’ decay, a statistical analysis of observations and numerical modeling of tsunami generation and propagation have been conducted. The main goal is to uncover physical mechanisms of the tsunami wave field formation and evolution at scales up to tens of thousands of kilometers in space and a few days in time. It is shown that the tsunami lifetime is related to the wave-energy diffusion and dissipation processes. The decay time of the variance of the tsunami-generated level oscillations is about 1 day. Multiple reflections and scattering by irregularities of the bottom topography make the field of the secondary tsunami waves stochastic and incoherent: the distribution of the wave energy in the ocean reaches a statistical equilibrium in accordance with the Rayleigh-Jeans law of equipartition of the wave energy per degree of freedom. After the tsunami front has passed, the secondary-wave energy density turns out to be inversely proportional to the water depth.

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 score0.642
Threshold uncertainty score0.443

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 studies0.0010.000
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 head0.021
GPT teacher head0.223
Teacher spread0.201 · 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

Citations12
Published2014
Admission routes1
Has abstractyes

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