A Comparative Study of the Methods to Assess Occupational Noise Exposures of Fish Harvesters
Bibliographic record
Abstract
BACKGROUND: Noise-induced hearing loss is a well-known occupational disease that affects many fish harvesters from many fisheries worldwide, whose risk factor is prolonged exposure to hazardous noise levels. To date, academic research activities and regulatory bodies have not provided any comparative analysis among the existing methods to assess noise exposure levels of fish harvesters. This paper provides a comparison of four relevant assessment methods of noise exposure, examining the results of a measurement campaign performed onboard small fishing vessels from Newfoundland and Labrador. METHODS: We traveled onboard 11 vessels engaged in multiple fisheries from Newfoundland and Labrador and performed extensive noise exposure surveys using the simplified International Maritime Organization method, the full-day measurement method, and the two methods provided by ISO 9612:2009, the task-based method and job-based method (JBM). RESULTS: The results showed that the four methods yield similar values when the noise components are dominated by the engine and auxiliaries (steady-state sources); when noise components are dominated by the fishing gear, task-based method and the simplified International Maritime Organization method estimates are less accurate than JBM, using full-day measurements as baseline. CONCLUSION: The JBM better assesses noise exposure in small-scale fisheries, where noise exposure has significant variance and uncertainties on the exposure levels are higher.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".