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Record W2092912163 · doi:10.1175/2007waf2005104.1

Modeling of Two Northwest Atlantic Storms with Third-Generation Wave Models

2007· article· en· W2092912163 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWeather and Forecasting · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsStormNested set modelMeteorologyEnvironmental scienceWave modelSignificant wave heightClimatologyWind waveGeologyComputer sciencePhysicsOceanography

Abstract

fetched live from OpenAlex

Abstract In this study, three state-of-the-art operational forecast wave models are implemented on nested grids in order to achieve fine-resolution wave simulations (0.1°) in the Gulf of Maine and related northwest Atlantic waters. These models are the Simulating Waves Nearshore (SWAN) model, the Wave Action Model (WAM), and WAVEWATCH-III (hereafter WW3). Model performance is evaluated through comparisons with field measurements. Four composite model systems are compared: WAM and WW3 implemented on three nested domains, SWAN nested within WAM, and SWAN nested within WW3. Storm case studies include two intense midlatitude winter storms from January 2000 and January 2002. Although the models are comparable in terms of their overall performance and skill, it is found that WW3 provides a better statistical fit to the observed wave data compared with the other models, and that SWAN gives slightly better results if nested within WW3, rather than within WAM.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.853

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)0.0000.000

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 head0.048
GPT teacher head0.210
Teacher spread0.162 · 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