Review of Weapon Noise Measurement and Damage Risk Criteria: Considerations for Auditory Protection and Performance
Bibliographic record
Abstract
Noise-induced hearing loss resulting from weapon noise exposure has been studied for decades. A summary of recent work in weapon noise signal analysis, current knowledge of hearing damage risk criteria, and auditory performance in impulse noise is presented. Most of the currently used damage risk criteria are based on data that cannot be replicated or verified. There is a need to address the effects of combined noise exposures, from similar or different weapons and continuous background noise, in future noise exposure regulations. Advancements in hearing protection technology have expanded the options available to soldiers. Individual selection of hearing protection devices that are best suited to the type of exposure, the auditory task requirements, and hearing status of the user could help to facilitate their use. However, hearing protection devices affect auditory performance, which in turn affects situational awareness in the field. This includes communication capability and the localization and identification of threats. Laboratory training using high-fidelity weapon noise recordings has the potential to improve the auditory performance of soldiers in the field, providing a low-cost tool to enhance readiness for combat.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".