MétaCan
Menu
Back to cohort
Record W4386959852 · doi:10.9753/icce.v37.papers.38

FLEXIBLE FLUID-STRUCTURE INTERACTION OF A FLEXIBLE PLANT MODEL FOR NATURE-BASED SOLUTIONS

2023· article· en· W4386959852 on OpenAlexaff
Ross Henteleff, Acacia Markov, Jacob Stolle, Ioan Nistor, V. P. Sriram

Bibliographic record

VenueCoastal Engineering Proceedings · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsInstitut National de la Recherche ScientifiqueWilfrid Laurier UniversityUniversity of Ottawa
Fundersnot available
KeywordsDragPaceFluid–structure interactionComputer scienceMarshMarine engineeringVariety (cybernetics)Field (mathematics)Accretion (finance)SoftwareSalt marshEnvironmental scienceGeologyAerospace engineeringEngineeringOceanographyFinite element methodPhysicsEcologyMathematicsGeodesyStructural engineering

Abstract

fetched live from OpenAlex

Nature-based solutions (NBS) represent a new field of research and engineering applications, becoming increasingly popular in the coastal engineering field. Salt marsh restoration, an example of NBS, is particularly appealing due to the variety of benefits they can provide, especially their capacity to induce sediment accretion, potentially keeping pace with sea-level rise. This study investigates the applicability of the flexible fluid-structure (FSI) interaction module being developed for open-source software REEF3D to the motion of marsh plants under wave action using data from a physical model study performed by Paul et al. (2016). The model consistently overestimates the drag force response of a flexible plastic plant surrogate under wave action. This suggests that this new tool may not be suited for this case. However, further investigation must be performed to test the limits of the model’s application.

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: none
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.218
Teacher spread0.201 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCoastal Engineering ProceedingsSame topicCoastal and Marine DynamicsFrench-language works237,207