Comparison of methods for the estimation of Young’s moduli and structural loss factor of wood, applied to guitar soundboards made of Adirondack spruce
Bibliographic record
Abstract
Standardised methods for characterising the mechanical properties of materials were mainly developed for metals or alloys with isotropic behaviour. They are consequently poorly suited to characterising materials that exhibit orthotropic and space-varying behaviour, such as polymers, composites, and natural materials like wood. The focus of this work is on guitar soundboards made of Adirondack spruce ( Picea rubens , also known as red spruce). Tests are conducted on 12 quarter-sawn plates. This work compares the practicality of methods to estimate the Young's modulus and the structural loss factor of these soundboards, while the variability of results for each method is outlined. A first interest of this work is to determine the trade-off between measurement methods and systems of variable cost and complexity. In addition, the viability of an asymptotic method is demonstrated for such structures (a technique based on mobility measurement and the assumption of an infinite structure). At the same time, the importance of considering the sensor mass is confirmed for low-density structures by comparison with measurements on aluminum and medium-density fibre board panels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".