Impulse measurement considerations in setting occupational noise criteria
Bibliographic record
Abstract
The most common occupational noise limits are 85 dBA Leq and 140 dBZpeak for 100 impulses.Simple arithmetic shows that 100 high frequency pulses at 140 dBZ for 0.9 msec each will give 85 dBA (assuming the A-weighting has little effect due to the frequencies involved), i.e. in practical terms the Leq limit will usually be exceeded before the impulse limit.To check this in practice over 400 measurements were reviewed from a smelting and casting facility and from an ore milling operation.These measurements included impulse noise from jack hammers, pneumatic motors and exhausts, heavy scrap dropping into bins, etc.In no case was 140 dB exceeded, although 85 dBA was exceeded in many cases.More important, in every case the 85 dBA Leq limit would be exceeded well before the 140 dBZpeak limit.It is well known that noise dosimeters are unreliable in measuring impulse noise due to false impulses caused by rubbing the microphone and cable.As a result, routine assessment o f impulse noise is much more difficult (expensive) than assessments using just Leq.It is concluded that in practice there is little advantage, and some decided disadvantages, to doing routine assessment (or regulation) of impulse noise exposure in industry.
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 | 0.044 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".