Comparison of direct measurement methods for headset noise exposure in the workplace
Bibliographic record
Abstract
Specialized equipment and techniques are required to carry out sound measurements under occluded ears for the purpose of assessing the noise exposure from communication headsets. Standard ISO 11904 describes two procedures: 1) the microphone in a real ear (MIRE) and 2) the acoustic manikin technique using an occluded ear simulator IEC 60318-4. Methods using simpler artificial ears, such as IEC 60318-1, have also been proposed in national occupational noise measurement standards. Such fixtures are more practical to use and more easily accessible. However, they have not been designed specifically for noise measurements under communication headsets and there is little comparative data to the manikin technique, which is considered the gold standard for simulated in-situ acoustic measurements. Fit-refit measurements were obtained under laboratory conditions with four measurement set-ups (artificial ear type 1, type 2, type 3.3, and acoustic manikin), three communication headset types (circum-aural, supra-aural, intra-aural) and six different communication signals. Data was transformed into equivalent-diffuse sound levels using third-octave procedures as well as single number corrections. Overall, results across measurement set-ups are in broad agreement, except for the type 1 artificial ear, but single number corrections reduce measurement accuracy.
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 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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".