Validation of a Finite Element Continuum Model of Vocal Fold Vibration
Bibliographic record
Abstract
Vocal fold dynamics have been explored extensively in recent years using both reduced-order coupled mass and continuum models. These models have been used for analysis of phonosurgeries and pathologies, and accordingly, require greater resolution and progressive simulation techniques to capture the defining characteristics of pathological and/or uncommon speech. The following work offers a contribution to the advancement of the aforementioned models through the development and validation of an in-house finite element model which captures the influence of a sessile vocal fold polyp. A validation of the adopted finite element formulation is presented through replication of previous computational studies on vocal folds under free vibration conditions. Natural frequencies and mode shapes are extracted from the computed eigensystems and subsequently compared with these studies to legitimize the formulation of the system’s equation of motion and the corresponding numerical solution. Convergence behaviour and accuracy of these analyses are used to justify the choice of formulation in the development of the code. The development of a model of a sessile polyp is documented, and a brief analysis of this system is presented. Fundamental frequency magnitude is found to be inversely proportional to polyp size, and at a minimum for the case of a polyp centered along the length of the vocal fold.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".