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Record W4323313521 · doi:10.3390/jmse11030555

Interactions between Surface Waves, Tides, and Storm-Induced Currents over Shelf Waters of the Northwest Atlantic

2023· article· en· W4323313521 on OpenAlexafffund
Shangfei Lin, Jinyu Sheng

Bibliographic record

VenueJournal of Marine Science and Engineering · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsDalhousie University
FundersOcean Frontier Institute
KeywordsCurrent (fluid)GeologyStormStorm surgeOceanographyOcean currentAnticyclone

Abstract

fetched live from OpenAlex

A coupled wave–tide–circulation model is used to investigate wave–current interactions (WCIs) over the shelf waters of the Northwest Atlantic (NWA) during Hurricane Earl (2010). WCIs have substantial impacts on hydrodynamics in the upper ocean. The significant wave heights are modulated by WCIs, particularly over regions with strong current gradients, with a reduction up to ~2.1 m (20%) during the storm. Noticeable decreases in surface elevations and tidal currents occur in regions with strong tides such as the Gulf of Maine, mainly due to the wave-enhanced bottom stress. Over regions with weak tidal currents, wave effects on currents are dominated by two competitive processes between wave-induced forces and wave-enhanced mixing. The former strengthens surface currents (up to ~0.55 m/s) and increases the peak storm surge (up to ~0.48 m). The latter is responsible for the reduction in storm-induced surface currents (up to ~0.94 m/s) and anticyclonic modulation of current directions. Vertically, WCIs extend the strong vertical current shear and shift it downward during the storm, which enhances the local mixing and changes the structures of near-inertial oscillations (NIOs). Moreover, tidal currents also change the magnitudes of the NIOs and subtidal currents and affect the intensity of WCIs.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.245
Teacher spread0.225 · 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 designObservational
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

Citations6
Published2023
Admission routes2
Has abstractyes

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