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Record W2910387390 · doi:10.1029/2018jc014167

An Enhanced Framework to Quantify the Shape of Impulse Waves Using Asymmetry

2019· article· en· W2910387390 on OpenAlexafffund
Gemma Bullard, Ryan P. Mulligan, W. Andy Take

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAsymmetryAmplitudeFlumeBreaking waveMechanicsPhysicsImpulse (physics)Wave flumeSurface waveMechanical waveGeologyWave propagationFlow (mathematics)OpticsLongitudinal waveClassical mechanics

Abstract

fetched live from OpenAlex

Abstract The shape of a wave generated by a landslide, snow avalanche, or fluid flow greatly influences its size and speed as it propagates away from the source region, which are critical parameters needed to estimate the impacts of these waves on coastal communities. In this study, laboratory data are produced from waves generated by the impact of water into a wave flume akin to the impact of a fluidized, highly mobile, and neutrally buoyant slide into a reservoir. Water surface observations are made using wave probes that remain at fixed positions, while the water depths and source volumes of slide material are varied. The wave shape is quantified by calculating the asymmetry about the vertical axis at each wave probe. The experimental results indicate that waves with positive or near‐zero asymmetry in the near field have a small influence on the maximum wave amplitude along the flume. However, waves with negative asymmetry in the near field change rapidly in shape and amplitude due to breaking until a stable state with symmetrical shape and wave breaking limit of 0.6 is reached. The length scale at which the breaking waves reach this state is quantified based on the initial asymmetry. An enhanced mathematical framework is developed using horizontal‐scale coefficients to modify the solitary wave equation such that it can be used to generate asymmetrical waves. This new method might be used in combination with predictions of the maximum wave amplitude to create time series needed to account for the shape of the tsunamis.

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 categoriesInsufficient payload (model declined to judge)
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 score0.818
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.363
Teacher spread0.334 · 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.

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

Citations20
Published2019
Admission routes2
Has abstractyes

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