Occupational Noise Exposure and Hearing Protector Use in Canadian Lumber Mills
Bibliographic record
Abstract
Noise exposure is probably the most ubiquitous of all occupational hazards, and there is evidence for causal links between noise and both auditory and nonauditory health effects. Noise control at source is rarely considered, resulting in reliance on hearing protection devices to reduce exposure. A comprehensive noise survey of four lumber mills using a randomized sampling strategy was undertaken, resulting in 350 full-shift personal dosimetry measurements. Sound frequency spectrum data and information on hearing protector usage was collected. A determinants-of-exposure regression model for noise was developed. Mean (L(eq,8hr)) exposure level was 91.7 dBA, well above the exposure British Columbia (BC) limit of 85 dBA. Of 52 jobs for which more than a single observation was made, only 4 were below the exposure limit. Twenty-eight jobs had means over 90 dBA, and four jobs had means over 100 dBA. The sawmill and by-products departments of the lumber mills had the highest exposure to low frequency noise, while the planing and saw filing areas had the highest exposure to high frequency noise. Hearing protector use was greatest among those exposed above 95 dBA, and among those exposed between 85 and 95 dBA, self-reported use was 84% for 73% of the time. The determinants of exposure model had an R(2) of 0.52, and the within-participant correlation was 0.07. Key predictors in the final model were mill; enclosure and enclosure construction material; and certain departments, jobs, and noise sources. The study showed that workers in lumber mills are highly exposed to noise, and although the prevalence of the use of hearing protection is high, their use is unlikely to provide complete protection again noise-induced hearing loss at the observed exposures. Determinants of noise exposure modeling offers a good method for the quantitative estimation of noise exposure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.000 |
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".