Special issues in hearing loss prevention in the Canadian military environment
Bibliographic record
Abstract
Noise can be particularly noxious to hearing in the military. The personnel regularly face a wide range of noise-hazardous situations, many of which are seldom encountered in other work environments. High noise levels are associated with the operation of small arms and large caliber weapons, combat vehicles, aircrafts, ships and vessels, and industrial equipment. This can induce permanent and temporary hearing loss, compromise speech communication, interfere with the detection and localization of sound sources and warning sounds and thus, can jeopardize life or safety of the personnel. This paper will review the essential elements of a hearing loss prevention program proposed for the Canadian Armed Forces. The ultimate goal is to preserve hearing health as well as all hearing abilities necessary for effective operations. The program has been designed to meet the noise measurement and hazard investigation procedures, limits on noise exposure, use of hearing protection and other regulatory measures contained in the Canadian Occupational Health and Safety (COHS) Regulations (Part VII: Levels of Sound), while addressing the particular nature of the military environment. The paper will focus on issues that are not typically found in other occupational environments (variable work schedules, excessive impulse noise, extended exposures, communication devices).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".