Lingual Behavior in Clarinet Articulation: A Multiple-Case Study Into Single and Double Tonguing
Bibliographic record
Abstract
Articulating notes on the clarinet requires the control of many factors, one of which is the behavior of the tongue. It is hypothesized that one of the mechanisms to produce notes in the altissimo (highest) register involves the lowering of the tongue dorsum. The study sought to answer the question of whether different tonguing techniques interfered with the required lowering of the tongue dorsum in this register, making adequate note production difficult. Four professional clarinet players performed diatonic scales across the chalumeau, clarion, and altissimo registers using two techniques—single and double tonguing. Movements of the tongue dorsum and tongue blade were recorded with 3D Electromagnetic Articulography. The movement data revealed that, for all players, a low position of the tongue dorsum was indeed associated with a higher success rate of producing adequate notes in the altissimo register. Single tonguing was the most effective technique due to ability of the tongue dorsum to lower during the highest register. For three of the four players, failed note production in the altissimo register when performing double tonguing related to a high tongue dorsum position; one participant, however, was successful in performing double tonguing in the altissimo register, despite a high tongue dorsum position. This latter finding suggests player-specific strategies to successfully realize double tonguing in the altissimo register.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".