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Record W4386960217 · doi:10.9753/icce.v37.structures.6

EFFECT OF GRAVEL PARTICLE SIZE ON THE RESHAPING OF DYNAMIC REVETMENTS

2023· article· en· W4386960217 on OpenAlex
Dario A. B. Sirianni, Ioan Nistor, Colin Rennie, Andrew Cornett, Enda Murphy, Scott Baker, David Hnatiw

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCoastal Engineering Proceedings · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
Fundersnot available
KeywordsCrestGeologyShoreGeotechnical engineeringSubmarine pipelineStormErosionBreaking waveHydrology (agriculture)GeomorphologyOceanographyWave propagation

Abstract

fetched live from OpenAlex

Gravel beaches are prevalent natural features on many coastlines and play a vital role in shore protection, often inspiring protection structures (Bayle et al., 2020) and nature-based solutions. Dynamic revetments are constructed features, designed to mimic gravel beaches, dissipating wave energy and preventing or limiting erosion. Compared to sand beaches, gravel revetments dissipate incident wave energy and dampen backwash intensity by percolating through the relatively large pore volume space (Komar, 2007). During storm events, gravel particles move up the beach, accreting at the crest. This predominately onshore-directed transport of gravel material differs from the storm-driven morphodynamic behaviour of sandy beaches, which tend to exhibit higher rates of offshore-directed transport (Komar, 2007). Ahrens’ Theorem continues to be used as a design volume estimator for dynamic revetments (Bayle et al., 2020). This primary objective of this study is to investigate how dynamic revetments with different D50 reshape under various wave conditions.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.599
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.200
Teacher spread0.193 · 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