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Record W1974416915 · doi:10.9753/icce.v32.currents.2

EXPERIMENTAL AND NUMERICAL MODELING OF TSUNAMI LOADING ON STRUCTURES

2011· article· en· W1974416915 on OpenAlexaff
Ioan Nistor, Dan Palermo, Andrew Cornett, Taofiq Al-Faesly

Bibliographic record

VenueCoastal Engineering Proceedings · 2011
Typearticle
Languageen
FieldEngineering
TopicEarthquake and Tsunami Effects
Canadian institutionsCanadian Cardiovascular SocietyUniversity of Ottawa
Fundersnot available
KeywordsCoastal engineeringShoreNumerical modelingComponent (thermodynamics)Field (mathematics)GeologyNumerical modelsMarine engineeringCivil engineeringEngineeringOceanographyGeophysics

Abstract

fetched live from OpenAlex

The results of a comprehensive research program on tsunami-induced forces on infrastructure located in coastal areas are presented. This research project is the result of an extensive interdisciplinary project (coastal and structural engineering) which spanned over the past five years and which included several field data reconnaissance missions, as well as analytical, experimental and numerical modeling of the extreme hydrodynamic forces on buildings and their component structural elements. The purpose of this research and engineering project is to elucidate the complex hydrodynamic mechanisms of the impact and extreme loadings on buildings and to properly quantify loads and further propose and derive new formulations for the design of structures located in the vicinity of the shoreline in tsunami-prone coastal areas.

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)0.001
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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.196
Teacher spread0.184 · 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

Citations43
Published2011
Admission routes1
Has abstractyes

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