Virtual and experimental acoustical comfort testers for earplugs
Bibliographic record
Abstract
Earplugs are widely used to prevent noise-induced hearing loss, but discomfort can reduce their effectiveness by affecting their consistent and proper use.Earplug comfort can be described by four dimensions: physical (biomechanical and thermal interactions with the earcanal), acoustical (noise/useful sound perception), functional (usability and efficiency), and psychological (well-being and satisfaction).(Dis)comfort results from the interplay within the user/earplug/work environment triad.The components of this triad and their interactions across multiple phases, ultimately shape the comfort judgment and are defined by various physical and psychological characteristics that must be assessed to fully understand comfort.This paper targets acoustical characteristics of both disposable and reusable earplugs when inserted in the earcanal, focusing on indicators such as sound attenuation and occlusion effect.It presents a synthesis of various acoustic comfort testers developed by the authors' research team to assess these characteristics.Virtual and physical truncated realistic artificial ears and whole head are explored.This research aims to provide manufacturers with comfort-driven design methods for earplugs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".