Vibrational analysis of arundo donax L (woodwind reed cane) through internal friction measurements and microstructure evaluation
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".