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Record W2020065960 · doi:10.2112/08-1016.1

Wave Transformation and Longshore Sediment Transport Evaluation for the Egyptian Northern Coast, via Extending Modern Formulae*

2009· article· en· W2020065960 on OpenAlexfundno aff
Mohammed Khalifa, M. A. El Ganainy, R. I. Nasr

Bibliographic record

VenueJournal of Coastal Research · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsnot available
FundersU.S. Army Corps of EngineersCanada Excellence Research Chairs, Government of Canada
KeywordsSubmarine pipelineSediment transportCoastal engineeringShoreLongshore driftPort (circuit theory)Wave heightGeologyOceanographySedimentMeteorologyEnvironmental scienceGeographyGeomorphologyEngineering

Abstract

fetched live from OpenAlex

Studies and evaluations were carried out for the purpose of analyzing satellite measured data, or carrying out preliminary evaluations for the study area, near Port Said. In this study waves were transformed from the offshore area to the nearshore area, and longshore sediment transport quantities and rates were evaluated by applying and adjusting some of the available modern formulae. This was done by calibrating with the available reference data, as given in the literature. Thus, the applicability of such formulae for that area can be checked. Offshore significant wave heights from altimeter measurements, which can be downloaded at www.waveclimate.com, are used in this research for 2003 through 2005. The study domain dimensions are 50 km long offshore and 25 km wide alongshore. Wave nearshore transformation is carried out by using the mathematical Simulating WAves Nearshore model (SWAN) based on seasonal/directional bases. Four seasons are considered, winter, spring, summer, and autumn, representing the whole year. Three bulk-type modern formulae, Coastal Engineering Research Center, U.S. Army Corps of Engineers (CERC), Kamphuis, and Van Rijn, for longshore transport evaluation are applied in the study area. The reference targets used for comparison are based on the literature. Through calibration with the reference targets and among themselves, the study comes up with some correction factors for both CERC and Van Rijn formulae to give quite realistic evaluations for that area. These extended/corrected formulae can also be applied in places with similar conditions to the Egyptian coast.

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)0.003
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.064
GPT teacher head0.328
Teacher spread0.264 · 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 designOther design
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

Citations9
Published2009
Admission routes1
Has abstractyes

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