Middle-ear Finite-Element Modelling with Realistic Geometry and a Priori Material-Property Estimates
Bibliographic record
Abstract
Finite-element models of the middle ear have generally included oversimplified geometries, and many of their material properties have often been estimated by curve fitting to averaged experimental vibration measurements from multiple ears. As a result, the parameter values may not be physiologically reasonable. Our study aims to construct a valid middle-ear finite-element model without such curve fitting by (1) creating realistic geometries, and (2) using a priori estimates for the material properties. We began by scanning a human temporal bone using x-ray microcomputed tomography. Details of middle-ear structures were then segmented, both manually and semi-automatically. The substructures were assigned appropriate material properties – including thickness, Young’s modulus, and Poisson’s ratio – based on a detailed literature review, and a finite-element model was generated. The static behaviour of this model was compared with lowfrequency measurements performed on the same temporal bone using laser Doppler vibrometry. Preliminary results show good model accuracy with regard to footplate and eardrum displacements, and agreement within a factor of about two for umbo displacement. A sensitivity test was done to identify those material properties which have strong effects on the model behaviour.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".