MétaCan
Menu
Back to cohort

SIMULATION OF CONTAINER DRIFT UNDER EXTREME HYDRODYNAMIC CONDITIONS

2023· article· en· W4386969175 on OpenAlex
Shin Yazaki, Ryota Nakamura, Ioan Nistor, Jacob Stolle

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
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversity of Ottawa
Fundersnot available
KeywordsDebrisContainer (type theory)Computer scienceGeologyEnvironmental scienceEngineeringOceanographyMechanical engineering

Abstract

fetched live from OpenAlex

In Great East Japan Earthquake of 2011, debris entrained within the incoming tsunami waves was responsible for widespread infrastructure damage (Chock et al., 2011). The presence of debris within the inundating wave needs to be taken into consideration; however, few methodologies and tools exist to estimate debris hazards. Therefore, one option to evaluate the behavior of drifting debris is using numerical models. Currently, the particle method, which is a meshless analysis method, is expected to be utilized for analyzing the behavior of drifting objects (Goaseberg et al., 2017). However, the ability of models to properly capture the interaction between multiple drifting objects has not yet been verified. This study will be among the first to model drift behavior of multiple debris under tsunami-like conditions, using the DualSPHysics model suite.

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: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.231
Teacher spread0.217 · 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