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Record W2064958002 · doi:10.1121/1.2956479

Vibrational characteristics of harp soundboards

2008· article· en· W2064958002 on OpenAlexafffund
Chris Waltham, A. Kotlicki

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2008
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsHARPModalString (physics)Register (sociolinguistics)Quality (philosophy)AcousticsBar (unit)PhysicsComputer scienceTheoretical physicsParticle physicsMaterials scienceMeteorology

Abstract

fetched live from OpenAlex

Harps exist in different forms, from large factory-made concert harps to small hand-made folk harps. This variety presents both a challenge and an opportunity for acousticians. The musical quality of a harp depends on many factors, but key among these is the soundboard. This work sets out to define some general desirable qualities of a harp soundboard. First, in order to understand the relationship between the vibrational behavior of a bare soundboard and that of a completed instrument, a 36-string harp was built from scratch. Measurements were made at each stage of construction, and the results showed how the bare soundboard properties affect those of the finished harp. Second, the soundboards of several harps of different sizes were assessed by measuring the admittances along the string bar. These data showed that one relationship crucial to the quality of the soundboard is that between the modal shapes and modal frequencies of the soundboard, and the position and fundamental frequencies of the strings attached to it. A general statement is made about this relationship, one which should be of use to harp makers.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.233
Teacher spread0.217 · 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

Citations17
Published2008
Admission routes2
Has abstractyes

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