In-ear noise dosimetry: Challenges and benefits
Bibliographic record
Abstract
Noise at work and noise induced hearing loss (NIHL) are worldwide major problems in many industries.Although efficient noise control measures should be promoted and be directly applied to the damaging noise sources, hearing protection devices (HPDs) remain currently the most commonly used defense against NIHL.Evaluating HPDs effectiveness in the workplace is particularly contingent upon two variables: the ambient noise level and the attenuation of the HPD.Unfortunately, in practical workplace conditions, a precise knowledge of these metrics is rather uncommon.Large imprecisions may lead to improper HPD selection and may result in workers being underprotected or even overprotected.To address this problem, recent researches have involved the development of in-ear dosimetric devices, specifically designed to monitor the noise exposure levels directly in the ear canal in real-time.This paper presents the scientific and technical challenges of such a research project that targets the development of two in-ear dosimetric devices: an earplug-type and an open-ear insert.The main research topics are presented and discussed.They are: i) determination of personalized relationships between in-ear and freefield levels; ii) effect of ear canal occlusion on hearing sensitivity; iii) effect of self-induced noise; iv) hardware implementation.Representative results are presented for each topic to illustrate how in-ear dosimetry can be efficiently implemented and offer clear benefits for hearing conservation programs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".