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Record W4400928440 · doi:10.1029/2023jc020630

Nearshore Flow Dynamics Over Shore‐Oblique Bathymetric Features During Storm Wave Conditions

2024· article· en· W4400928440 on OpenAlexafffund
Laura Szczyrba, Ryan P. Mulligan, Peir K. Pufahl, J. L. Humberston, Jesse McNinch

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsQueen's University
FundersCoastal and Hydraulics LaboratoryEngineer Research and Development CenterU.S. Army Corps of EngineersNatural Sciences and Engineering Research Council of CanadaQueen's UniversityOregon State University
KeywordsBathymetryShoreGeologySubmarine pipelineOblique caseStormLongshore driftCurrent (fluid)OceanographySediment transportSedimentGeomorphology

Abstract

fetched live from OpenAlex

Abstract Shore‐oblique bathymetric features occur around the world and have been statistically correlated with enhanced shoreline retreat on sandy beaches. However, the physical mechanisms that explain a causal relationship are not well understood. In this study, radar remote sensing observations and results from a phase‐resolved numerical model explore how complex morphology alters nearshore hydrodynamics. Observations at selected times during high‐energy storm events as well as a suite of idealized simulations indicate that shore‐oblique features induce strong spatial variations in the water surface elevation, wave breaking patterns, and mean current pathways. Re‐emergent offshore flows and longshore current accelerations occur near the shoreward apex of the oblique nearshore features. The results suggest that complex bathymetric morphology exerts a powerful control on nearshore hydrodynamics and increases the potential for enhanced cross‐shore and alongshore sediment transport.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.019
GPT teacher head0.292
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes2
Has abstractyes

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