Extensive hearing loss induced by low‐frequency noise exposure
Bibliographic record
Abstract
Background: With little attention given to low-frequency traffic noise and our understanding that cochlear function may be highly susceptible to low-frequency noise, there is an urgent need to determine traffic noise-induced hearing loss (NIHL), not only the hearing loss at low frequency but also the possible high-frequency hearing loss. Methods: The current study aims to investigate the potential for extensive hearing loss induced by exposure to 0.063 kHz octave band noise (OBN), which is an important component of low-frequency traffic noise. The threshold of auditory brainstem response (ABR) was used to evaluate hearing function before and after noise exposure. Chinchillas were randomly assigned into seven different groups. Group 63-3 h/6 h, Group 2 k-3 h/6 h, and group 4 k-3 h/6 h were exposed for either 3 or 6 h to 0.063, 2, and 4 kHz OBN at 90 dB SPL, respectively. The control group was not exposed to noise. Results: Significant ABR threshold-shifts (TS) were observed at 0.88, 2, 4, and 5.7 kHz in Group 63-6 h, and at 2.8 and 4 kHz in Group 2 k-6 h, and at 5.7 kHz in Group 4 k-6 h. ABR-TS were consistent with outer hair cell (OHC) losses, exposure to 0.063 kHz OBN at 90 dB SPL for 6 h induced large-scale losses of OHC both in low- and high-frequency region. Conclusions: Exposure to 0.063 kHz low-frequency OBN at 90 dB SPL for 6 h leads to significant hearing loss over an extensive range from low to high frequencies.
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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 | 0.000 |
| 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 | 0.000 | 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".