Hearing, communication and cognition in low-frequency noise from armoured vehicles
Bibliographic record
Abstract
An experiment was performed to study auditory perception and cognitive function in the presence of low-frequency dominant armoured vehicle noise (LAV III). Thirty-six normal hearing subjects were assigned to one of three noise backgrounds: Quiet, pink noise and vehicle noise. The pink and vehicle noise were presented at 80 dBA. Each subject performed an auditory detection test, modified rhyme test (MRT) and cognitive test battery for three different ear conditions: Unoccluded and fitted with an active noise reduction (ANR) headset in passive and ANR modes. Auditory detection was measured at six 1/3 octave band frequencies from 0.25 to 8 kHz. The cognitive test battery consisted of two subjective questionnaires and five performance tasks. The earmuff, both in the conventional and ANR modes, did not significantly affect detection thresholds at any frequency in the pink and vehicle noise backgrounds. For the MRT, there were no significant differences between the speech levels required for 60% correct responses for three ear conditions in the pink and vehicle noise backgrounds. A small but significant (4 dB) increase in speech level was required in pink noise as compared to vehicle noise. For the serial reaction time task, the mean response time in the vehicle noise background (751 ms) was significantly higher than in pink noise and quiet (709 and 651 ms, respectively). The mean response time in the pink noise background was also significantly higher than in quiet. Thus, the presence of noise, especially low-frequency noise, had a negative effect on reaction time.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".