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Record W4386103922 · doi:10.31399/asm.amp.2023-05.p013

Identification of Sustainable Tonewoods for Acoustic Guitars Using Materials Selection Software

2023· article· en· W4386103922 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAM&P Technical Articles · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGuitarSustainabilityIdentification (biology)Sustainable developmentSelection (genetic algorithm)Computer scienceMaterial selectionEngineeringEnvironmental economicsBusinessConstruction engineeringManufacturing engineeringEcologyEconomicsMaterials scienceManagementArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract A search for alternative, sustainable tonewood species has been prompted by constraints in availability and concerns about ecological impacts associated with established sources. This article describes an effort to identify tonewood substitutes for guitar soundboards using the ANSYS Granta Selector based on derived performance objectives for physical and mechanical properties. These objectives are then combined with those for species morphology, producibility, and sustainability to arrive at sustainable and viable tonewood substitution options that meet manufacturer and customer expectations.

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.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.100
GPT teacher head0.286
Teacher spread0.186 · 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