MétaCan
Menu
Back to cohort
Record W2966435027 · doi:10.1016/j.joes.2019.07.003

Application of SWAN model for storm generated wave simulation in the Canadian Beaufort Sea

2019· article· en· W2966435027 on OpenAlexafffundabout
Md. Azharul Hoque, William Perrie, S. Solomon

Bibliographic record

VenueJournal of Ocean Engineering and Science · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsNatural Resources CanadaBedford Institute of Oceanography
FundersNatural Resources CanadaFisheries and Oceans Canada
KeywordsStormBuoyGeologyMeteorologyBeaufort seaWind waveWave modelWinter stormClimatologySignificant wave heightSea iceOceanographyPhysics

Abstract

fetched live from OpenAlex

The wave model SWAN (Simulating WAves Nearshore) is implemented for the Canadian Beaufort Sea and storm generated waves are investigated through comparisons between in situ buoy observations and numerical simulations. Simulations are performed for four storms using the SWAN wave model. We specifically use SWAN's non-stationary and two-dimensional modes in a fine resolution nested domain within a coarse resolution domain. Two established whitecapping formulations in SWAN are examined; one is dependent on mean spectral wave steepness and the other is on local spectral steepness. Model simulations in the shallow fine resolution domain also consider the effects of bottom friction and nonlinear triad interactions. For the Beaufort Sea study area, wave simulations in which the white capping formulation is dependent on local spectral steepness are better than those where the dependency is on mean spectral steepness; however implementation of bottom friction term and triad mechanisms in the present study does not lead to any notable enhancement in the simulations.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.206
Teacher spread0.192 · 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

Citations57
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Ocean Engineering and ScienceSame topicOcean Waves and Remote SensingFrench-language works237,207