Comparison of different types of hearing protection devices for use during weapons firing
Bibliographic record
Abstract
Introduction: Hearing protection devices (HPDs) are not rated for impulse noise, which makes it difficult to select an appropriate device for use during weapons firing. Measurements of different types of HPDs (level independent, level dependent, earplugs, and earmuffs) were performed following American National Standards Institute/Acoustical Society of America S12.42 procedures. Methods: A rifle producing a peak level of 154 decibels of sound pressure level was used as the noise source for the measurements. The devices tested were E-A-R Classic earplugs, Combat Arms double-end earplugs, ETY·Plugs earplugs (standard fit), Peltor H10A earmuffs, and Peltor PowerCom Plus earmuffs. The earmuffs were also tested in combination with the E-A-R Classic earplugs and with ballistic glasses. Results: The earplug and earmuff combinations provided the most protection, followed by the level-independent earplugs, earmuffs, and level-dependent earplugs. Wearing of ballistic glasses reduced the effectiveness of the earmuffs. Discussion: Although the results provide information about the level of protection that is possible for several types of HPDs, the best choice of HPD depends on the operational setting. The combination of level-independent earmuffs and earplugs was the most effective, but it is not a practical solution when communication is required. Earplugs should be worn in cases in which earmuffs interfere with sighting the weapon or are incompatible with other gear such as a helmet or glasses. Future work should include different types of communication headsets and different combinations of HPDs.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".