Comparison of direct measurement methods for headset noise exposure in the workplace
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The measurement of noise exposure from communication headsets poses a methodological challenge. Although several standards describe methods for general noise measurements in occupational settings, these are not directly applicable to noise assessments under communication headsets. For measurements under occluded ears, specialized methods have been specified by the International Standards Organization (ISO 11904) such as the microphone in a real ear and manikin techniques. Simpler methods have also been proposed in some national standards such as the use of general purpose artificial ears and simulators in conjunction with single number corrections to convert measurements to the equivalent diffuse field. However, little is known about the measurement agreement between these various methods and the acoustic manikin technique. Twelve experts positioned circum-aural, supra-aural and insert communication headsets on four different measurement setups (Type 1, Type 2, Type 3.3 artificial ears, and acoustic manikin). Fit-refit measurements of four audio communication signals were taken under quiet laboratory conditions. Data were transformed into equivalent diffuse-field sound levels using third-octave procedures. Results indicate that the Type 1 artificial ear is not suited for the measurement of sound exposure under communication headsets, while Type 2 and Type 3.3 artificial ears are in good agreement with the acoustic manikin technique. Single number corrections were found to introduce a large measurement uncertainty, making the use of the third-octave transformation preferable.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it