A geometrically nonlinear finite-element model of the cat eardrum
Bibliographic record
Abstract
Current finite-element (FE) models of the eardrum are limited to low pressures because of the assumption of linearity. Our objective is to investigate the effects of geometric nonlinearity in FE models of the cat eardrum with an approximately immobile malleus for pressures up to +/-2.2 kPa, which are within the range of pressures used in clinical tympanometry. Displacements computed with nonlinear models increased less than in proportion to applied pressure, similar to what is seen in measured data. In both simulations and experiments, there is a shift inferiorly in the location of maximum displacement in response to increasingly negative middle-ear pressures. Displacement patterns computed for small pressures and for large positive pressures differed from measured patterns in the position of the maximum pars-tensa displacement. Increasing the thickness of the postero-superior pars tensa in the models shifted the location of the computed maximum toward the measured location. The largest computed pars-tensa strains were mostly less than 2%, implying that a linearized material model is a reasonable approximation. Geometric nonlinearity must be considered when simulating eardrum response to high pressures because purely linear models cannot take into account the effects of changing geometry. At higher pressures, material nonlinearity may become more important.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".