Towards a revised international standard for personal sound exposure meters
Bibliographic record
Abstract
Personal sound exposure meters and personal noise dosimeters are designed to be worn on a person and to measure sound exposure or sound exposure as a percentage of a predetermined criterion. Different jurisdictions employ different definitions of these quantities and different criteria for exposure. Existing normative standards for these instruments therefore differ in their specifications and are outdated due to changes in markets and technologies since their publication. The International Electrotechnical Commission is revising the applicable international standard (IEC 61252) to modernise and harmonise requirements for the instruments. The technical aims of the revision are to provide realistic specifications, methods for testing all relevant characteristics of a model of personal sound exposure meter, and methods for periodic testing of individual instruments. Specifications need to reflect the actual practice of measurements of noise exposure worldwide and test methods need to be consistent, realistic, and affordable. The needs of various users have been surveyed and used to prepare substantial technical changes to the specifications in the document. Experience with the equivalent international standard for sound level meters (IEC 61672) has been reviewed to draft new test methods that are appropriate for personal sound exposure meters.
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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 | 0.033 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.011 |
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".