Relative importance and interactions of parameters of finite-element models of human middle ear
Bibliographic record
Abstract
In the last decades, finite-element models of the middle ear have been widely used to predict the middle-ear vibration outputs. Even with the simplest linear assumption for material properties of the structures in the middle ear, these models need tens of parameters. Due to the complexities of measurements of material properties of these structures, accurate estimations of the values of most of these parameters are not possible. In this study, we benefited from the stochastic finite-element model of the middle ear we had developed in the past, to perform global sensitivity analysis. For this aim, we implemented Sobol' sensitivity analysis which ranks the importance of all uncertain parameters and interactions among them at different frequencies. To decrease the computational costs, we found Sobol' indices from surrogate models that we created using stochastic finite-element results and the polynomial chaos expansion method. Based on the results, the Young's modulus and thickness of the tympanic membrane, Young's modulus and damping of the stapedial annular ligaments, and the Young's modulus of ossicles are among the parameters with the greatest impacts on vibrations of the umbo and stapes footplate. Furthermore, the most significant interactions happen between the Young's modulus and thickness of the tympanic membrane.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".