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Record W2323910964 · doi:10.1142/9789814277426_0230

SURFACE GRAVITY WAVE INTERACTIONS WITH DEEP-DRAFT NAVIGATION CHANNELS – PHYSICAL AND NUMERICAL MODELING CASE STUDIES

2009· article· en· W2323910964 on OpenAlexaff
Shubhra K. Misra, Andrew Driscoll, James T. Kirby, Andrew Cornett, Pedro Lomónaco, Otavio J. Sayao, Majid Yavary

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeophysics and Sensor Technology
Canadian institutionsCanadian Cardiovascular Society
Fundersnot available
KeywordsHullSurface waveSurface (topology)GeologyComputer scienceMarine engineeringAerospace engineeringEngineeringTelecommunicationsGeometryMathematics

Abstract

fetched live from OpenAlex

This paper addresses the interactions between surface gravity waves and deep-draft and wide navigation channels with steep side slopes, through numerical and physical modeling case studies. The underlying physical processes are illustrated and the consequences to port master planning, harbor agitation and design of coastal structures in proximity to navigation channels are discussed through detailed analysis of numerical and physical model response to the channel. A comparative evaluation of several numerical modeling paradigms exposes the strengths and limitations of each formulation when applied to describe such interactions. The consequences to the planning of numerical and physical modeling studies are discussed. To the authors ’ knowledge, this is the first comprehensive evaluation of wave-channel interactions with bathymetric gradients that define the steep side slopes of navigation channels that are becoming increasingly common in the continued expansion and design of existing and new ports and harbors.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.260
Teacher spread0.240 · 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 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

Citations5
Published2009
Admission routes1
Has abstractyes

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