Validation of a finite element code for a continuum model of vocal fold vibration under the influence of a sessile polyp
Bibliographic record
Abstract
Vocal fold vibration has been extensively investigated using numerical simulation through the use of lumped element models, and more recently, through the use of finite element continuum models. Finite element models offer the ability to analyze the effects of detailed and complex geometric models, allowing for the study of the influence of pathologies and phonosurgery on the process of phonation. The present study details the development of a finite element code of vocal fold vibration and a continuum model of a vocal fold with a sessile polyp. The capability of the code to capture major structural vibration trends are illustrated through a validation process, wherein previously explored models are replicated, and computed results are sub- sequently compared to gauge the code’s efficacy. An overview of literature pertinent to the modeling of vocal fold polyps is presented, followed by the discussion of the creation of a continuum model of a vocal fold affected by a sessile polyp. This pathological model is parameterized according to the size and position of the polyp, and trends are explored based on varying these parameters. Polyp size is concluded to have a more profound influence on the fundamental frequency of vibration than position. An inversely proportional relationship is found between polyp size and fundamental frequency, as well as proximity to the anterior-posterior surface and fundamental frequency.
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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 | 0.003 |
| 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".