The effect of firefighter personal protective equipment on auditory thresholds
Bibliographic record
Abstract
Communication on a fire scene is essential to the safety of firefighters. Not only to be able to hear and understand radio chatter, but also alarm signals used on the fireground. One such alarm is the Personal Alert Safety System (PASS) device. This device is used to help locate a downed firefighter. One part of this complex problem is the effect of the protective equipment (helmet, eye protection, hood, coat) on hearing. Previous findings have shown the effect of this protective equipment on head related transfer functions using a KEMAR. [Suits et al. (2013, June). Paper presented at the International Congress on Acoustics, Montreal, Canada] The physical acoustic measurements showed a change in the signal that would reach the tympanic membrane. To relate the findings of the physical measurements to human reactions, the change in auditory threshold caused by wearing the personal protective equipment was measured. The changes seen in the physical acoustics measurements caused the auditory threshold of the subjects to increase at higher frequencies. The measured increases at 3000 Hz, 4000 Hz, and with an example PASS signal were between 5 and 10 dB. [Work supported by U.S. Department of Homeland Security Assistance to Firefighters Grants Program.]
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".