An indirect calculation method for estimating occupational sound exposure from communication headsets
Bibliographic record
Abstract
Measurement of noise exposure from communication headsets is challenging due to the need for specialized equipment, methods, and training. Canadian standard Z107.56-13 [(2013). Measurement of Noise Exposure (Canadian Standards Association, Mississauga, Canada)] introduced a calculation procedure to promote a simpler method that stakeholders in hearing loss prevention could readily apply using widely accessible sound level equipment. The original procedure specified a fixed signal-to-noise ratio (SNR) of 15 dB above the protected background noise when estimating the speech listening level through the headset communication channel. The relationship between background noise level, noise type, headset noise reduction (NR), and speech listening level is revisited in this study. In a noise simulation room, 24 participants were asked to adjust the headset volume while listening to speech and executing a visual reaction task. Results indicate that the growth in speech listening levels with noise is not adequately represented by a fixed SNR, and that one-sided listening increases speech levels by about 5-7 dB in quiet and in noise compared to two-sided listening. Moreover, use of an octave-band procedure with derating to estimate headset NR best captured the speech listening level data. A revised calculation procedure based on linear regression modeling is described with parameters adjusted separately for one-sided and two-sided headsets.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".