Colt C8A2 Carbine Impulsive Noise Auditory Hazard Assessment through Testing In a Reverberant Environment
Bibliographic record
Abstract
Exposure to high levels of gunfire impulsive noise is potentially hazardous to human hearing and may lead to permanent auditory as well as non-auditory damage in the unprotected ear. Correctional Service Canada (CSC) approached NRC Aerospace to address a concern for potential hearing damage from discharging of firearms within enclosed spaces such as armoured control posts. In order to address this concern, a reconfigurable control post was built by NRC on a shooting range and this test setup used to measure the impulsive noise time trace waveforms. The measured waveforms were analyzed in accordance with the updated standard procedure MIL-STD-1474E (revision 15 April 2015) using the algorithm suggested by the standard: Auditory Hazard Assessment Algorithm for Humans (AHAAH). This algorithm enabled the assessment of the auditory hazard risk to which personnel would be exposed during a Colt C8A2 carbine discharge. Three different geometry and floor area control post configurations were considered. Moreover two different control post window configurations where considered, namely: a) armored window with a narrow slit gun port opening surrounded by ballistic glass and b) wide open large window. The paper presents the results and conclusions of the data analysis of the testing campaign aimed at evaluating the noise exposure and assessing the auditory hazard of personnel using the Colt C8A2 carbine.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".