Localization of reverse alarms with personal safety equipment
Bibliographic record
Abstract
Several factors can contribute to the occurrence of accidents involving reversing heavy vehicles, despite the mandatory use of reverse alarms in many workplaces. Among others, reverse alarms can be difficult to localize in space, which may lead to errors in adequately identifying the source of danger. Previous studies have shown that traditional reverse alarms (“beep-beep”) are more difficult to localize in space than broadband alarms (“pschtt-pschtt”). In addition, personal safety equipment such as hearing protection devices and safety helmets, often required in noisy workplaces where reverse alarms are used, may potentially further impair localization. This study explored the effect of passive hearing protection devices (earplugs, earmuffs and double protection) and use of a safety helmet on the ability of normal-hearing individuals to localize the two types of reverse alarms, in background noise, while performing a task. Consistent with previous findings, the broadband alarm was easier to localize than the tonal alarm. While passive hearing protection can have a significant impact on sound localization (with a marked degradation in performance with double protection), use of a safety helmet has a more limited effect. Preliminary results from a study using the same methodology with level-dependent (sound restoring) hearing protection devices are also presented.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".