Influence of the external ear tissue domains on the sound attenuation of an earplug predicted by a finite element model
Bibliographic record
Abstract
Earplugs are a widespread solution to prevent the problem of hearing loss in the workplace environment, but they do not always perform as desired. Using a model of the ear canal occluded by an earplug could be helpful to perform sensitivity analyses (geometry and materials of the earplug) and to better understand the role of the earplug. The human external ear is a complex system made up of different tissues with a 3D geometry. In practice, it is reduced to a 2D cylindrical geometry for the acoustical tests fixtures. The purpose of this study is to compare the insertion loss predicted by a 3D complex finite element model of the ear canal surrounded by different tissue domains (skin, soft tissue, and bone) and occluded by a silicon earplug versus a 2D axisymmetric model of the same system. In both models, some investigations are made in order to verify if the models could be simplified by replacing the tissue domains by mechanical impedances. These investigations are made to reduce the complexity of the models and to discuss the relevance of whether or not including external ear tissue domains in a sound attenuation model of an earplug.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".