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Record W2898064518 · doi:10.1121/1.5068441

Violin surface velocities and radiativity

2018· article· en· W2898064518 on OpenAlexaff
Chironjeev Kanjilal, Chris Waltham

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2018
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsUniversity of British Columbia Hospital
Fundersnot available
KeywordsMicrophoneAnechoic chamberAcousticsHammerViolinMaterials scienceDirectivityAccelerometerOpticsRADIUSPhysicsLoudspeakerComputer scienceAntenna (radio)Telecommunications

Abstract

fetched live from OpenAlex

Complete surface velocity maps and radiativity measurements have been made of several violins of varying quality. The instruments were excited laterally at the top of the bridge with a small impact hammer, giving a strong signal up to 4 kHz. The wood velocities were measured with a 0.2 g accelerometer placed sequentially on a grid, all over the surface. The grid was small enough to allow a detailed look at the bridge island. The f-hole velocities were measured with a 6 mm diameter microphone at nine positions per hole, and the acoustic pressures were converted to velocities with a propagating wavefront model. The surface measurements were compared to radiation measurements made with a 92 cm radius microphone array in an anechoic chamber. The surface measurements could be completed in a day, the radiativities in two hours. The resulting maps of operating deflection shapes, calibrated in m/s per N, allow a detailed examination of the vibrational behavior of each instrument, at frequencies up to and including the first torsional mode of the bridge.

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)0.002
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 score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.012
GPT teacher head0.241
Teacher spread0.228 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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Same venueThe Journal of the Acoustical Society of AmericaSame topicMusic Technology and Sound StudiesFrench-language works237,207