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Record W3010393472 · doi:10.1080/21664250.2020.1723194

Evaluation of force exerted by tetrapods displaced by tsunami on caisson breakwater return wall

2020· article· en· W3010393472 on OpenAlexaff
Go Hamano, Hidenori Ishii, Kotaro Iimura, Tomoyuki Takabatake, Jacob Stolle, Miguel Esteban, Tomoya Shibayama

Bibliographic record

VenueCoastal Engineering Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicEarthquake and Tsunami Effects
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsBreakwaterCaissonFlumeArmourGeotechnical engineeringGeologyStructural engineeringEngineeringMechanicsLayer (electronics)Materials sciencePhysicsFlow (mathematics)

Abstract

fetched live from OpenAlex

Tsunamis can cause large damage to breakwaters and other protection structures placed along the coastline. The strong flows generated by such waves can move the armor units of composite breakwaters, leading to large impact forces on the caissons behind them. In the present work laboratory experiments were carried out in a flume to determine the forces that can result from such impacts. Then, an empirical equation that would allow the estimation of these forces was proposed, with the aim of facilitating the construction of returning walls and parapets situated on top of caissons.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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 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

Citations2
Published2020
Admission routes1
Has abstractyes

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