Noise exposure from communications headsets: The effects of environmental noise, attenuation and SNR under the device
Bibliographic record
Abstract
The Canadian studies on headset exposure at various industrial sites and Crabtree are reviewed to gain more insight into the main determinants of headset sound exposure and to provide an empirical basis for the new calculation method under the CSA WG. The field method require two similar communication headset, one worn by the worker to carry out normal tasks and one placed on the manikin to measure sound levels under the device. The correlation coefficient shows that about 95% of the noise variation in headset sound level is explained by the environmental background noise around the user. The slope of regression line is found to be 0.42, which shows that the headset exposure rose by only 0.42 dB for each 1 dB increase in background noise over the data set. The headset equivalent sound levels and the background noise is found to be +12 to +15 dB in the quieter settings and -5 to 0 dB in the noisier settings.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".