Virtual and experimental physical comfort testers for earplugs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Earplugs are commonly used to prevent noise-induced hearing loss, but their efficacy is often hindered by discomfort, impacting consistent and correct use. Comfort of earplugs can be comprehended through four dimensions: physical (related to biomechanical and thermal interactions with the earcanal), acoustical (linked to noise perception), functional (including usability and efficiency) and psychological (related to well-being and satisfaction). The evaluation of (dis)comfort involves intricate interactions among components of a triad formed by the user, the earplug, and the work environment. Recent research by the authors has identified key psychosocial and physical characteristics of the triad influencing earplug physical discomfort. This study examines specific physical characteristics of the coupling between the "user" and "earplug" components for disposable and reusable earplugs. Virtual and experimental comfort testers serve as modeling tools and test benches to enable this determination. Mechanical comfort testers of increased complexity designed to assess tribological characteristics of the earplug/earcanal system are introduced. The study starts with simple benches measuring radial forces, extraction forces and friction coefficients, progressing to more advanced tools assessing mechanical pressure in various earcanal shapes either with rigid walls or lined with skin. This work aims at providing earplugs comfort-driven design methods for manufacturers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it