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NUMERICAL MODELING OF DEBRIS IMPACTS USING THE SPH METHOD

2014· article· en· W2069374979 on OpenAlex

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 · 2014
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsDebrisSmoothed-particle hydrodynamicsElevation (ballistics)Environmental scienceCurrent (fluid)ReplicateGeologyTsunami waveOceanographySeismologyMechanicsPhysics

Abstract

fetched live from OpenAlex

The significance of coastal forests as a protection barrier against tsunami waves has been of particular interest following recent tsunami events. Coastal forests have been shown to attenuate tsunami-induced inundation and are believed to be capable of reducing the propagation of tsunami-borne debris onshore. The current paper aims to examine the suitability of using a Smoothed Particle Hydrodynamics (SPH) model to (1) simulate debris impact forces acting on a structure and (2) to determine if it is possible for a small coastal forest to attenuate tsunami-borne debris. The results of this study indicate that the SPH model utilized was able to reasonably replicate the hydrodynamic forces acting on structures and the water surface elevation, but was not able to reproduce the large debris impact forces observed in an experimental test program. However, the authors concluded that coastal forests can potentially provide protection against floating debris.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.778
Threshold uncertainty score0.545

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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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