A three-dimensional finite-element model of a human head for predicting the objective occlusion effect induced by earplugs
Bibliographic record
Abstract
In a noisy environment, wearing a correctly fitted earplug is the sine qua non condition to prevent noise-induced hearing loss. However, this condition is often unfulfilled due to the discomforts induced by the wearing of the earplug, among which the acoustical discomfort is influenced by the occlusion effect. Objectively, this phenomenon is quantified by a low frequency amplification of the sound pressure in the earcanal, induced by bone-conduction when the earcanal is occluded. Numerical models can go beyond the practical and ethical limits of experiments on living humans. Thus they can be an efficient and helpful tool to evaluate the occlusion effect. They also make it possible to better understand its underlying physical mechanisms associated with different factors concerning the anatomy, earplug and stimulation and ultimately how to reduce it. Thereby, a three-dimensional finite-element model of a human head is developed to compute the occlusion effect induced by earplug under a bone-conducted stimulation. Good agreement is obtained between the simulation results and the experimental data available in the literature giving confidence in the model to predict the occlusion effect. The model is exploited to investigate the individual effects of various factors (e.g., earplug and tissue properties) on the occlusion effect.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".