Sound attenuation from earmuffs and earplugs in combination: maximum benefits vs. missed information.
Bibliographic record
Abstract
INTRODUCTION: Noise levels from military aircraft range from 100-130 dBA. Peak pressure levels from large caliber weapons may reach 180 dB SPL. To protect against hearing loss, individuals are encouraged to wear double hearing protection. This study determined ways to maximize benefit. METHOD: Hearing thresholds from 0.25-8 kHz and consonant discrimination were assessed in normal-hearing subjects with ears unoccluded and fitted with highly rated earmuffs and earplugs, singly or in combination. The earplugs were available in two sizes. Selection was based on best fit. Attenuation values were derived from the threshold measurements. RESULTS: With the muff, plug, and muff and plug in combination, thresholds ranged from 35-48 dB SPL, 40-55 dB SPL, and 44-66 dB SPL, respectively, across the frequencies tested. The combination (without regard to size of plug) resulted in attenuation values of 38-54 dB. With the smaller of the two plugs, low-frequency values as high as 53-61 dB were realized. Consonant discrimination decreased by 6-8% with the devices worn singly and by 22% with the devices in combination, relative to unoccluded listening. DISCUSSION: Sufficient low-frequency attenuation may be achieved with muffs and plugs in combination to prevent hearing loss from operational noise. Attenuation may be maximized by choosing a smaller earplug to achieve a better fit. Possible downsides are reduced detection of warning sounds and speech intelligibility. To be heard warning sounds should surpass protected thresholds by at least 5 dB. Choosing devices which provide somewhat less attenuation may be necessary to preserve communication capability.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".