Hearing protection devices and methods used for their evaluation: A military perspective
Bibliographic record
Abstract
Introduction: Soldiers are regularly exposed to potentially harmful noise such as the constant noise of transport vehicle engines and the impulse noise of weapons. Impulse noise can be particularly hazardous, especially, for example, the high-intensity noise of artillery or shoulder-fired projectile launchers. After administrative and engineering controls, hearing protection devices (HPDs) are a cornerstone of hearing conservation programs. Yet selecting the appropriate protection for the various mission tasks must be done with care. HPDs can range from simple earplugs to high-tech options. Methods: Methods of characterizing the attenuation of HPDs against high-level impulse noise are complex and evolving. Results: For the soldier, the need to balance the degree of measured sound attenuation against interference with other auditory abilities – such as the need to hear soft sounds, to understand commands, or to localize sound – is a common dilemma. Discussion: This article outlines some of the challenges of assessing and choosing HPDs that keep soldiers safe from noise exposure with a view to helping those new to hearing conservation understand more about this important subject.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.064 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".