Evaluation of a calculation method of noise exposure from communication headsets
Bibliographic record
Abstract
Specialized standardized methods for the measurement of noise exposure from communication headsets or sound sources close to the ear include the Microphone in Real Ear and manikin techniques, as specified in ISO 11904-1/2. The 2013 version of Canadian standard Z107.56 introduced a simpler calculation method to increase accessibility to communication headset exposure assessments for the widest range of stakeholders in hearing loss prevention. The calculation method only requires general and widely accessible sound measurement equipment and basic computational steps that account for the main determinants of exposure such as the background noise around the user, the sound attenuation of the communication headset, and the expected communication duration and effective listening signal-to-noise ratio. This paper reviews recent research on the effects of the spectral and temporal characteristics of the background noise and the headset configuration on the speech listening level. Results indicate that the listening level is largely insensitive to spectral and temporal variations in the background noise and that A-weighted noise level is a good predictor of listening level once headset attenuation is taken into account. It is also found that one-sided headsets increase exposure by 6-7 dB compared to two-sided headsets due to binaural summation.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".