Comparison of sound propagation and perception of three types of backup alarms with regards to worker safety
Bibliographic record
Abstract
A technology of backup alarms based on the use of a broadband signal has recently gained popularity in many countries. In this study, the performance of this broadband technology is compared to that of a conventional tonal alarm and a multi-tone alarm from a worker-safety standpoint. Field measurements of sound pressure level patterns behind heavy vehicles were performed in real work environments and psychoacoustic measurements (sound detection thresholds, equal loudness, perceived urgency and sound localization) were carried out in the laboratory with human subjects. Compared with the conventional tonal alarm, the broadband alarm generates a much more uniform sound field behind vehicles, is easier to localize in space and is judged slighter louder at representative alarm levels. Slight advantages were found with the tonal alarm for sound detection and for perceived urgency at low levels, but these benefits observed in laboratory conditions would not overcome the detrimental effects associated with the large and abrupt variations in sound pressure levels (up to 15-20 dB within short distances) observed in the field behind vehicles for this alarm, which are significantly higher than those obtained with the broadband alarm. Performance with the multi-tone alarm generally fell between that of the tonal and broadband alarms on most measures.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".