Bibliographic record
Abstract
The effect of hearing protective earmuffs which incorporate active noise reduction (ANR) on sound source identification was studied. The purpose was determine whether ANR interfered with the encoding of cues normally used for directional hearing. Right/left, front/back and within quadrant confusions were assessed in quiet using a circular array of eight loudspeakers. Three stimuli, one-third octave bands centred at 0.5 kHz and 4 kHz and broadband noise, were presented. These enabled an assessment of the utilization of mainly interaural time-of-arrival and level differences, and binaural and spectral cues in combination, respectively. Two groups of normal hearing subjects aged 18-30 and 40-55 years, half male and half female, participated. Overall, age, gender, and ANR were not significant determinants of outcome. The probably of correctly discriminating among the eight speakers decreased significantly with the muffs worn, relative to unoccluded listening by 10%, 35% and 40% for the 0.5 kHz, 4 kHz and broadband stimuli, respectively. The pattern of errors indicated that the earmuffs interfered with the encoding of both binaural (interaural level differences) and spectral cues. With ANR small additional right/left confusions were observed for the low-frequency stimulus (time-of arrival cue) for speakers close to the midline axis. The results provide further evidence that earmuffs should not be used in situations where the perception of the direction of hazard is a concern. ANR technology does not appear to increase the handicap.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".