Bibliographic record
Abstract
Experimental techniques for investigating the piano hammer-string interaction are described. It is argued that the accuracy, consistency, and scope of conclusions of previous studies can be compromised by limitations of the conventional methods relating to key inputs; physical distortion; numerical distortion, particularly when differentiation or integration of measured signals is used to derive primary response variables; contact identification; and synchronization issues. These problems are discussed, and experimental methods that have been devised to avoid them are described and illustrated by detailed results from a study of the hammer-string interaction in a vertical piano. High resolution displacements are obtained directly by non-contact high-speed imaging and quantitative motion tracking. The attention focused on achieving very accurate and consistent temporal and spatial alignment, including the objective procedure used for contact identification, allows meaningful comparisons of responses from separate tests. String motion at the strike point and on each side of it, as well as hammer motion, is obtained for eight dynamic levels from 1.06 to 2.98 m/s impact velocity. Detailed observations of the force-compression behavior of the hammer interacting with real strings are presented. The direct effects of hammer shank deflection and agraffe string pulses on the interaction are also highlighted.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".