Toward an improved hearing safety standard for impulse noise exposure in the Canadian Armed Forces
Bibliographic record
Abstract
Introduction: Current hearing safety standards for the Canadian Armed Forces (CAF) do not adequately address exposure limits for mitigation of noise-induced hearing loss (NIHL) from weapon noise. Recommendations for updating the hearing safety standard are being formulated by the recently initiated Noise Health Hazard Working Group (NHHWG). In this article, we aim to provide a way forward for the NHHWG. Methods: On the basis of experience with auditory research, noise measurement, acoustic standards development, interactions with CAF members, and a review of the literature, we present an overview of the current state of knowledge regarding impulse noise. The topics include impulse noise measurement, engineering and administrative controls for noise exposure, and use of personal hearing protection devices (HPDs). Results: Although technology for impulse noise measurements and ways to account for HPDs have improved substantially in recent years, not much has changed in hearing damage risk criteria. Energy-based metrics can account for the impulse duration and frequency-dependent characteristics of the HPD, which are important considerations in calculating allowed exposures. Discussion: The NHHWG is recommended to focus on measurements of current weapons systems in various configurations and training environments and to evaluate the use of energy-based metrics, together with frequency-dependent HPD insertion loss.
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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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".