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Record W3028364805 · doi:10.3390/jmse8050361

Prediction of Shoreline Evolution. Reliability of a General Model for the Mixed Beach Case

2020· article· en· W3028364805 on OpenAlexfundno aff
Giuseppe Roberto Tomasicchio, Antonio Francone, David Simmonds, Felice D′Alessandro, Ferdinando Frega

Bibliographic record

VenueJournal of Marine Science and Engineering · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsnot available
FundersRegione PugliaUniversità della CalabriaCanada Excellence Research Chairs, Government of Canada
KeywordsShoreSediment transportGeologyReliability (semiconductor)Beach nourishmentSedimentHydrology (agriculture)OceanographyGeotechnical engineeringGeomorphology

Abstract

fetched live from OpenAlex

In the present paper, after a sensitivity analysis, the calibration and verification of a novel morphodynamic model have been conducted based on a high-quality field experiment data base. The morphodynamic model includes a general formula to predict longshore transport and associated coastal morphology over short- and long-term time scales. With respect to the majority of the existing one-line models, which address sandy coastline evolution, the proposed General Shoreline beach model (GSb) is suitable for estimation of shoreline change at a coastal mound made of non-cohesive sediment grains/units as sand, gravel, cobbles, shingle and rock. In order to verify the reliability of the GSb model, a comparison between observed and calculated shorelines in the presence of a temporary groyne deployed at a mixed beach has been performed. The results show that GSb gives a good agreement between observations and predictions, well reproducing the coastal evolution.

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)0.002
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.191
Teacher spread0.176 · 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

Citations27
Published2020
Admission routes1
Has abstractyes

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