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Record W2333539778 · doi:10.11159/ijmem.2012.001

Effects of a Scalloped and Rectangular Brace on the Modeshapes of a Brace-Plate System

2012· article· en· W2333539778 on OpenAlexvenueno aff
Patrick Dumond, Natalie Baddour

Bibliographic record

VenueInternational Journal of Mechanical Engineering and Mechatronics · 2012
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBraceOrthodonticsStructural engineeringEngineeringMedicine

Abstract

fetched live from OpenAlex

Shaping the soundboard braces on a wooden stringed musical instrument has long been a way in which instrument makers optimize their musical instruments. Reasons for these methods are scientifically not well understood. Various bracing patterns have successfully been used to create different-sounding wooden stringed musical instruments. These bracing patterns stimulate the modeshapes that are specific to the soundboard of the instrument. However, a higher adjustment resolution is required in order to specify the frequency spectrum of the musical instrument. This paper demonstrates how the shape of the braces affects the modeshapes of the vibrating system. A simple analytical model composed of a plate and brace is analyzed in order to see these effects. The results are plotted together for three cases: the plate by itself, the plate with a rectangular brace and the plate with a scalloped brace. For clarity, the modeshapes are analysed in 2D at different locations and along both the x and y directions of the plate. It is shown that any brace affects modeshapes for which the brace does not run along a nodal line. The different shapes of the brace are shown to affect different modeshapes by various degrees. If braces are stiffened at locations of maximum amplitude for a given modeshape, then that modeshape will be significantly affected. It is clear that by properly designing the shape of a brace, instrument makers can exert great control over the shape of the instrument’s modeshapes and therefore also their frequencies.

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 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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.006
GPT teacher head0.203
Teacher spread0.198 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations8
Published2012
Admission routes1
Has abstractyes

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