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Record W2317854292 · doi:10.2166/wst.2014.162

Sea wave propagation from offshore to Maputo's coast. Application to longshore sediment transport assessment

2014· article· en· W2317854292 on OpenAlexfundno aff
Cristina N. A. Viola, Manel Grifoll, Jaime Palalane, Tiago C. A. Oliveira

Bibliographic record

VenueWater Science & Technology · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsnot available
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsSediment transportSubmarine pipelineWave flumeGeologyLongshore driftSedimentOceanographyPeninsulaCoastal engineeringSedimentary budgetWave heightHydrology (agriculture)Wave propagationGeomorphologyBreaking waveGeotechnical engineeringGeography

Abstract

fetched live from OpenAlex

This study aims to characterize the wave climate near the coastal region of Maputo (Mozambique), and to provide a first assessment of the sediment transport load in this area. A time-series of 13 years' worth of offshore wave data, obtained from reanalysis products, was propagated to the coast. Wave propagation was performed using Linear Wave theory and the numerical model, Simulating WAves Nearshore (SWAN). Propagations with SWAN were carried out considering different scenarios in order to evaluate the influence of parameters such as wind, tidal level, frequency spectrum and numerical mesh resolution on wave characteristics along the coast. The prevalent waves propagated came from between east and southwest directions. Results from linear propagation were used to estimate the potential longshore sediment transport. The Coastal Engineering Research Center formula was applied for a stretch of beach in the Machangulo Peninsula. A net potential rate of longitudinal sediment transport (of the order of 10(5) m(3)/year, along an extension of the coast of 21 km) was directed northwards, and was consistent with the frequent wave directions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.623

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 science0.0010.000
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 head0.006
GPT teacher head0.206
Teacher spread0.201 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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