THE OPPORTUNITIES AND CHALLENGES OF IN-EAR NOISE DOSIMETRY
Bibliographic record
Abstract
Despite many efforts to reduce sound at the source, noise at work remains a major problem in many industries. While personal hearing protection devices (HPDs) are currently the most commonly used defense against noise induced hearing loss (NIHL), their effectiveness is particularly contingent upon two variables: the ambient noise level and the attenuation of the HPD. Yet in most cases, those two metrics are not precisely known, which leads workers to be inadequately protected. To overcome this problem, recent studies have involved the development of in-ear dosimetric HPDs that are meant to monitor the protected noise exposure levels in real-time. These can offer clear benefits for hearing conservation as they should finally permit to determine whether a given worker is properly protected against noise and, thus, help increasing the effectiveness of HPDs. But to achieve such result, additional research is needed to make the noise levels measured in the occluded ear canal truly representative of the noise dose effectively received by the worker. This research should deal with the acoustical corrections due to the Transfer Function of the Open Ear as well as the resonance between the hearing protector and the subject’s eardrum. It should also address the issues regarding the noise induced by the wearer himself and a potential change in noise susceptibility provoked by the occlusion of the ear canal. Future results should offer additional insight on the use of in-ear noise dosimetry to prevent NIHL.
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.
How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".