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Record W2513517420 · doi:10.1063/1.4962241

Acoustic buffeting by infrasound in a low vibration facility

2016· article· en· W2513517420 on OpenAlexafffund
Benjamin P. MacLeod, Jennifer E. Hoffman, Sarah A. Burke, D. A. Bonn

Bibliographic record

VenueReview of Scientific Instruments · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsAcousticsInfrasoundVibrationInertiaSound pressureBlock (permutation group theory)Finite element methodAeroelasticityPhysicsCoupling (piping)Mechanical engineeringEngineeringAerodynamicsMechanics

Abstract

fetched live from OpenAlex

Measurement instruments and fabrication tools with spatial resolution on the atomic scale require facilities that mitigate the impact of vibration sources in the environment. One approach to protection from vibration in a building's foundation is to place the instrument on a massive inertia block, supported on pneumatic isolators. This opens the questions of whether or not a massive floating block is susceptible to acoustic forces, and how to mitigate the effects of any such acoustic buffeting. Here this is investigated with quantitative measurements of vibrations and sound pressure, together with finite element modeling. It is shown that a particular concern, even in a facility with multiple acoustic enclosures, is the excitation of the lowest fundamental acoustic modes of the room by infrasound in the low tens of Hz range, and the efficient coupling of the fundamental room modes to a large inertia block centered in the room.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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 designBench or experimental
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

Citations3
Published2016
Admission routes2
Has abstractyes

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