Bibliographic record
Abstract
A series of auditory cues were designed to assist firefighters with navigation and general safety in a fire emergency. Firefighters must maintain situational awareness at all times and this can be lost with disorientation, which is one of the main causes of injury and even death. Disorientation can be caused by restricted vision due to heavy smoke, a lack of familiarity with the surroundings as well as hearing and communication difficulties caused by the intensity of the fireground sounds. Five professional firefighters were interviewed to identify ways in which auditory affordances could be used to support their work. Existing sounds from both the emergency environment and those generated by firefighting equipment were assessed to determine their importance in maintaining situational awareness. Noise reduction technology was investigated, to assess its potential use in limiting the levels of noise exposure experienced. A series of auditory cues were designed to address the issues that were found using binaural spatialization and Augmented Reality methods. A prototype system was presented to firefighters to determine its effectiveness. The firefighters found that noise reduction would be effective in improving their situational awareness and ability to communicate effectively. Additionally, the firefighters found that spatially placed auditory cues had the potential to be effective in navigation and orientation in a fire emergency. The findings suggest that the use of noise reduction and auditory affordances have the potential to improve situational awareness for firefighters, increase safety and potentially save lives.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 teacher head, 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".