Occupational noise exposures in aquaculture: assessment and mitigation strategy
Bibliographic record
Abstract
Noise-induced hearing loss has become an increasing concern for employees in the aquaculture industry. Deafness, hearing loss and hearing impairment have all been identified as some of the most common injury claims from aquaculture labourers. Despite this information, noise levels and associated noise exposures in facilities have been highly undocumented. This research aims to document information on noise exposure in aquaculture and identify short- and long-term solutions to high exposures experienced by employees. Data was collected at four aquaculture facilities in Canada. Noise sources were identified and analyzed in narrowband frequency. Noise exposures were also measured and compared with the recommendations outlined by the Canadian Standards Association. Exposures were observed to be highest during tasks within the vicinity of machinery and other mechanical equipment. Short-term solutions were identified through the selection of appropriate hearing protection. Engineering design solutions were then applied to assess the feasibility of long-term solutions to reduce exposures in facilities. Numerical acoustic simulations were performed on a facility model where the Design of Experiments methodology was applied to validate its acoustical properties. The simulations showed that design solutions could be applied to reduce noise transmission and lower exposure levels throughout the facility.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".