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Record W4404110871 · doi:10.3390/jmse12111997

On the Influence of Beach Slope on Wave Non-Linearities on a Macrotidal Low-Tide Terrace Beach

2024· article· en· W4404110871 on OpenAlexaff
Amadou Diouf, France Floc’h, Bamol Ali Sow, Charles Caulet, Emmanuel Augereau

Bibliographic record

VenueJournal of Marine Science and Engineering · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsUniversité du Québec à Rimouski
FundersAgence Nationale de la RechercheInstitut national des sciences de l'UniversISblue
KeywordsGeologyTerrace (agriculture)SkewnessShoreStormBeach nourishmentPlageGeomorphologyOceanographyGeography

Abstract

fetched live from OpenAlex

This study examines the evolution of wave shapes as they propagate over a beach of varying morphology, information essential for understanding coastal dynamics and supporting coastal management. Our objective was to analyze the relationship between wave shape parameters and the local slope of the beach. To achieve this, we used data from pressure sensors and topographic measurements to evaluate the shape of waves on a cross-shore profile of a low-tide terrace beach. The analysis of wave conditions revealed a pronounced modulation of the tidal signal, which is augmented during storm events. Our findings demonstrate that the asymmetry and skewness parameters are more pronounced in the reflective zone of the beach. Considering these results, it can be concluded that the non-linearity of waves is significantly affected by the beach slope. The parameterization method employed in this study effectively incorporates this factor, offering improved accuracy in comparison to the existing approaches.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.190
Teacher spread0.183 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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