Voice-Calling Detection Distance with a Parabolic Microphone in Land Search and Rescue
Bibliographic record
Abstract
Introduction The ability of a 55.9 cm parabolic microphone to increase detection range in search-and-rescue (SAR) sound sweeps for the purpose of responsive lost-person searching was examined. Methods Five SAR personnel listened for 3 random words shouted once by persons simulating a responsive lost person at a target loudness of 88 dB (at 1 m) at various distances in coniferous lodgepole pine and deciduous aspen parkland forests. Intelligibility distance (where 50% of the shouted words were understood) and audibility distance (where 50% of the shouted words were audible but not intelligible), along with visual detection range, were determined. The lost person’s unaided ear audibility of 5 whistle models and 1 portable train horn, blown at each searcher's parabolic microphone audibility distance, was also determined. Results The parabolic microphone significantly increased both intelligibility and audibility distance by an average factor of 1.44 compared with the unaided ear. Intelligibility distance d i pmic with the parabolic microphone was well predicted by the equation d i p m i c = 8805 e − 0.0978 d B a m b , where dB amb is ambient dB at the listening location. Only the portable train horn could be heard by all participants with the unaided ear at the audibility distance of the parabolic microphone. Conclusions The use of a parabolic microphone significantly increased auditory detection range. When combined with the tested portable train horn, our data suggests that SAR sound sweeps for a responsive subject with a parabolic microphone can expect area coverage rates 44% greater than with the unaided ear and approximately 20 times that of visual searching.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".