Attenuation of hearing protectors: A systematic comparison of subjective and objective measurement methods
Bibliographic record
Abstract
A key component when selecting a hearing protector is the noise attenuation offered by the device. The subjective Real-Ear Attenuation at Threshold (REAT) test method is the most commonly used procedure to measure attenuation. On the other hand, with the increase popularity of individual fit testing and miniaturization of electronic components, the Microphone-In-Real-Ear approach (MIRE), and its field counterpart F-MIRE, are becoming more appealing and well suited for estimating attenuation in laboratory or in “real world” occupational conditions. In this approach, two miniature microphones are used to measure sound pressure levels in the ear canal under the protector and outside of the protector. This study presents a systematic evaluation of the various factors relating the subjective and objective attenuation values. Experiments on human subjects were carried out where the subjects were instrumented on both ears with microphones outside and underneath their protector. They were then asked to go through a series of subjective hearing threshold measurements followed immediately by microphone recordings using high level broadband noises. Earmuffs, earplugs, and dual-protection were tested for each subject. The various factors relating the subjective and objective attenuation data are first presented. Results showing the relative importance of these factors are presented and discussed as well as various comparisons obtained with the different attenuation values.
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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 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".