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Record W2333881103 · doi:10.1061/40926(239)129

Wave Evolution on Fluid Mud Bottom

2007· article· en· W2333881103 on OpenAlexaff
Kevin R. Hall, Ali Oveisy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEarthquake and Tsunami Effects
Canadian institutionsQueen's University
Fundersnot available
KeywordsDissipationGeologyMechanicsSurface waveBreaking waveComputer simulationGeotechnical engineeringWave propagationPhysicsOptics

Abstract

fetched live from OpenAlex

It is well known that surface water waves interact with the fluid mud on the sea bed. Wave-mud interaction results to high wave energy dissipation and mud mass transport. This kind of wave energy dissipation, which generally is much more significant than wave dissipation due to bottom friction, should be considered in simulation of wave evolution and transformation in muddy coastal environments. This paper presents a numerical model of wave evolution and dissipation on fluid mud. For this purpose, the spectral decay of wave due to mud is implemented in SWAN (a third generation numerical model for Simulating WAves Nearshore) using a multilayered wave-mud interaction model. In this numerical model it is assumed that mud layer has a visco-elasic-plastic behavior.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.820

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.007
GPT teacher head0.197
Teacher spread0.189 · 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

Citations5
Published2007
Admission routes1
Has abstractyes

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