An investigation of noise levels in Alberta sawmills
Bibliographic record
Abstract
BACKGROUND: Noise exposure in the sawmill industry is an area of concern. This study documents the level of noise exposure in nine sawmills in the province of Alberta, Canada. METHODS: Personal noise monitoring data were collected in nine Alberta sawmills, in winter and in summer (n = 213). Exposures were considered in light of an estimated "real world" noise reduction rating (NRR) calculation assuming use of conventional hearing protection. Limited comparisons were made with spot area monitoring data. RESULTS: Only 10% of the personal monitoring measurements were below the Alberta 8-hr exposure limit of 85 dBA. Twenty-seven percent of the personal monitoring measurements were 95 dBA or higher. Worker enclosures played a large role in reducing noise exposure. There were no significant differences between seasons in noise category distributions (P = 0.61). The planermen and planer infeed operators had the highest percentage of personal monitoring measurements 95 dBA or higher (62% and 82%, respectively). CONCLUSIONS: Based on a conservative formula, a risk of excess noise exposure could exist even when wearing required hearing protection due to very high noise levels found in planing operations in sawmills.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".