Bayesian inference of tissue properties from glottal area waveforms using a 2D finite element model
Bibliographic record
Abstract
Bayesian inference has recently been demonstrated to be effective in estimating stationary and non-stationary reduced-order vocal fold model parameters, along with the associated levels of uncertainty, from simulated glottal area waveform measures (Hadwin et al., 2016, 2017). In these studies, the fitting model was a three mass body-cover model with two degrees of freedom in the cover layer. While demonstrative, restricting the fitting model to two degrees of freedom assumes a priori that this is sufficient to capture salient vocal fold dynamics, thus limiting future clinical applicability. To overcome this, we employ Bayesian inference to directly estimate tissue properties of a two-dimensional (2D) finite element vocal fold model from glottal area waveforms generated by both numerical simulations and recorded videos of synthetic vocal fold oscillations. We demonstrate that the 2D finite element model is not only capable of producing meaningful estimates with reasonable uncertainties, but is also capable of distinguishing between different experimental tissue properties, which is an essential step towards the development of patient specific models.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".