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Record W2610960210 · doi:10.1121/2.0000418

Vibrational analysis of arundo donax L (woodwind reed cane) through internal friction measurements and microstructure evaluation

2016· article· en· W2610960210 on OpenAlexaff
Connor Kemp, Gary Scavone

Bibliographic record

VenueProceedings of meetings on acoustics · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBamboo properties and applications
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
Fundersnot available
KeywordsArundo donaxMaterials scienceShakerVibrationComposite materialMicrostructureCaneAcousticsPhysicsEcologyChemistryBiology

Abstract

fetched live from OpenAlex

Natural cane reeds (Arundo Donax L and here termed ADL) have been used on woodwind instruments for centuries with little change. Problematically, their consistency, variable performance, durability and sensitivity to ambient conditions make it difficult for the musician to find and maintain a reed that responds to their liking. In this study material, microstructural and anatomical properties of the reed are examined and their contributions to vibrational performance analyzed. Measures of material damping (internal friction) are obtained through observations of phase lag between input stress and output strain waveforms. The effects of mechanically induced vibrations on these measurements are also investigated through the use of a shaker rig inducing small amplitude, fixed-free vibrations up to 440Hz. Experimental samples are examined under optical microscope for characterization of several microstructural features. It is shown that ADL fiber size is positively correlated with a reduction in damping (loss angle, δ) after exposure to oscillatory stresses. Additionally, vessel diameter (lumen) is negatively correlated with changes in δ values. Overall, mechanical vibrations simulating in-vivo conditions are found to affect δ values with time. Future work will include the comparison of the results with those obtained from reeds that are regularly played by a professional musician.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.164

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 head0.039
GPT teacher head0.246
Teacher spread0.207 · 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 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

Citations2
Published2016
Admission routes1
Has abstractyes

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