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Record W3084988918 · doi:10.2749/newyork.2019.2644

Super-long span bridge aerodynamics: on-going results of the TG3.1 benchmark test – Step 1.2

2019· article· en· W3084988918 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

VenueReport · 2019
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsRowan Williams Davies & Irwin (Canada)
Fundersnot available
KeywordsAeroelasticityAerodynamicsBenchmark (surveying)Bridge (graph theory)Wind tunnelStructural engineeringSpan (engineering)Stability (learning theory)Computer scienceTurbulenceEngineeringAerospace engineeringPhysicsGeologyMeteorologyMedicineMachine learning

Abstract

fetched live from OpenAlex

<p>This paper is part of a series of publications aimed at the divulgation of the results of the 3-step benchmark proposed by the IABSE Task Group 3.1 to define reference results for the validation of the software that simulate the aeroelastic stability and the response to the turbulent wind of super-long span bridges. Step 1 is a numerical comparison of different numerical models both a sectional model (Step 1.1) and a full bridge (Step 1.2) are studied. Step 2 will be the comparison of predicted results and experimental tests in wind tunnel. Step 3 will be a comparison against full scale measurements.</p><p>The results of Step 1.1 related to the response of a sectional model were presented to the last IABSE Symposium in Nantes 2018. In this paper, the results of Step 1.2 related to the response long-span full bridge are presented in this paper both in terms of aeroelastic stability and buffeting response, comparing the results coming from several TG members.</p>

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score0.414

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 head0.005
GPT teacher head0.201
Teacher spread0.196 · 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