Progress in assessing and limiting occupational noise exposures in the United States
Bibliographic record
Abstract
Occupational noise exposures in the United States (US) have resulted in a substantial occupational health burden on US workers. Despite the presence of regulations mandating exposure assessment and hearing loss prevention measures for highly-exposed workers, the prevalence of noise-induced hearing loss continues to be high. However, several recent efforts may result in progress in reducing exposures to, and health impacts from, occupational noise. First, the American Conference of Governmental Industrial Hygienists has recently proposed revisions to the organization’s Threshold Limit Value (TLV) for Audible Sound, formerly referred to as noise. The proposed revised TLV includes updated documentation as well as notes related to non-auditory health effects of noise (including cardiovascular disease and injuries), which have not previously been considered in an occupational noise exposure limit. Second, a measurement-based national Job Exposure Matrix for Occupational Noise in the US and Canada has been developed at the University of Michigan and is now publicly available (noisejem.sph.umich.edu). This resource represents a useful tool for better understanding exposures to noise by job and industry. These two efforts, along with others, represent important progress in enhancing occupational health by protecting workers from the adverse health effects of noise.
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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".